CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 6, NO. … · Index Terms—Industrial internet of...

CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 6, NO. 1, MARCH 2020 1

Open Ecosystem for Future Industrial Internet ofThings (IIoT): Architecture and Application

Pinjia Zhang, Senior Member, IEEE, Yang Wu, Student Member, IEEE, and Hongdong Zhu

Abstract—Advanced sensing, data analysis and communicationtechniques have led to the emergence and tremendous develop-ment of industrial internet of things (IIoT) in recent years, whichraises revolution in condition monitoring and maintenance forelectrical assets. An open ecosystem for future IIoT is proposed inthis paper and the architecture of the open ecosystem is discussed.An open development environment needs to be established forusers to interact with power devices and servers freely via web ormobile applications installed on user terminals, increasing IIoTscalability and flexibility. The core technologies of open ecosystemin future IIoT are discussed, which include comprehensivesensing techniques, wide-area communication tools, Big Dataservice infrastructure, data analysis algorithms and intelligentmaintenance schemes. An application of future IIoT ecosystem inwind farm maintenance is then presented. It is demonstrated thatthe efficiency and effectiveness of wind farm maintenance can beimproved with the support of open ecosystem of future IIoT,providing an innovative insight for electrical assets monitoringand maintenance with high reliability.

Index Terms—Industrial internet of things (IIoT), IIoT-based development environment, open ecosystem, wind farmmaintenance.

I. INTRODUCTION

PAST century has witnessed industrial revolution andinternet revolution where machines and distributed infor-

mation networks powered economy growth, and next centurywill be dominated by industrial internet of things (IIoT) whichprovides interconnection between machines and informationnetworks [1]. A world economic forum predicts that 92 milliondollars can be saved by connecting vehicles on the roadwith Internet by 2016 and 30 billion dollars can be saved inaviation industry by IIoT technologies. This value increasesto 66 billion in thermal-mechanical engineering industry and90 billion in petroleum industry. The development of IIoTalso provides innovative solutions for other industry in savingoperation cost and improving system reliability [2].

Manuscript received August 6, 2019; revised November 20, 2019; acceptedDecember 16, 2019. Date of publication March 30, 2020; date of currentversion January 4, 2020. This work was supported in part by the BeijingMunicipal Natural Science Foundation (L161002), and in part by the NationalNatural Science Foundation of China under Grant 51822705, 51777112 and61703227.

P. J. Zhang (corresponding author, email: [email protected]) and Y.Wu are with the Department of Electrical Engineering, Tsinghua University,Beijing 100084, China.

H. D. Zhu is with the Envision Energy Company, Jiangsu 214400, China.DOI: 10.17775/CSEEJPES.2019.01810

IIoT originates from internet of things (IoT) where commonobjects are regarded as things connected to an informationinternet. Supported by advanced sensing, data analysis andcommunication tools, IoT has introduced tremendous changesfor human daily routines [3], [4]. Various intelligent deviceshave already been widely used to monitor user health condi-tion [5] or provide health care services [6]. Aided with IoTmethods, smart homes can be realized where homeowners areable to monitor all movements around the house with sensorsand adjust electrical equipment operation mode with remotecontrol [7]. Smart parking can also be achieved with electricalvehicle charging and discharging in an optimal schedule,which improves system efficiency and reduces energy wasting,leading to future smart cities [8].

IIoT was first introduced by GE in 2012 by adopting con-sumer IoT technologies into industry, both from manufacturingand nonmanufacturing aspects [9]. This concept was thensupported by Cisco, IBM, Intel and AT&T in 2014 IndustrialInternet Consortium [10]. Sensors are attached to machines orelectrical assets, forming intelligent machines which uploadoperation information to a server instantaneously. Based oncollected sensor signals and designed data analyzing algo-rithms, the condition monitoring of electrical assets can berealized. System operation information can be further trans-mitted and displayed on web & mobile terminals by commu-nication tools, providing reference for maintenance staff [11].IIoT has great potential in various industrial sectors, such asmanufacturing, retailing, energy utilities, etc. [12]. Researcheshave proved that system reliability and effectiveness can belargely improved by utilizing IIoT technologies in powerindustry [13]–[15].

To date, researches on IIoT mainly focus on data com-munication, storage or application technologies. Due to thevariety of IIoT terminals, traditional TCP/IP-based networkcommunication infrastructure becomes invalid. To ensurecommunication security while improving system efficiency,software-defined networking (SDN) has received much atten-tion. The flexible, programmable and self-configuration natureof SDN guarantees a faster and more reliable communicationscheme [16]. Various kinds of algorithms including optimiza-tion algorithms and edge computing tools are also introducedto optimize SDN platforms [17]. Besides, as huge amount ofdata will be generated and stored in distributed or centralizedservers in IIoT, advanced Big data technologies including datastorage and analysis techniques are required [18]. Data securityalso becomes an essential issue, which can be solved byBlockchain diagram [19] with credit-based algorithms [20].

2096-0042 © 2019 CSEE

2 CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 6, NO. 1, MARCH 2020

Extracting useful information of electrical assets operationstates is the main aim of IIoT. With adequate data, machineoperation status can be obtained using machine learning [21]or deep learning algorithms [22], providing guidance forelectrical assets control scheme manipulation.

It can be noticed that although many researches have beenconducted on IIoT technologies, few studies concern aboutIIoT structure. In other words, most IIoT is designed forcertain functions, unable to be extended or adjusted basedon different user demands. Operating under a fixed and rigidpattern, IIoT functions are designated by developers at thebeginning [23]. Users have few accesses to IIoT data andprograms, which largely restricts the applications of IIoTunder various user requirements and operation environment.With increasing complexity of power system and industry,traditional IIoT fails the demands for flexible and efficientsystem monitoring and maintenance. An open ecosystem forfuture IIoT is proposed in this paper to improve systemflexibility and effectiveness. The key feature of the proposedfuture IIoT ecosystem lies in its bidirectional openness, withan interactive and intelligent working pattern.

The first contribution of this paper consists of proposinga novel architecture of IIoT, which is the open ecosystemfor future IIoT. The open ecosystem enables free interactionsbetween different terminals. IIoT functions and applicationscan be largely extended based on comprehensive systeminformation and active user participation.

The second contribution consists of analyzing the requiredcore technologies in the open ecosystem. Comprehensive sens-ing techniques ensure the scalability of the IIoT, while wide-area communication raises possibility for system openness.IIoT efficiency and effectiveness are guaranteed by Big Dataframework and advanced data analyzing algorithms. Intelligentcontrol and maintenance can be realized eventually.

In the third contribution, an application of future IIoTopen ecosystem in wind farm maintenance is analyzed todemonstrate the efficiency and effectiveness of the proposedarchitecture.

The rest of this paper is organized as follows. Architectureof the proposed open ecosystem for future IIoT is introducedin Section II, with core technologies presented in Section III.Section IV discusses a typical application of open IIoT in windfarm maintenance both from sensor and intelligent turbineaspects. Future trends and works to be done are also listed,providing an inspiring insight for applications of future IIoTin improving power system reliability and efficiency. Theconclusion of this paper is included in Section V.

II. ARCHITECTURE OF THE OPEN ECOSYSTEM FORFUTURE IIOT

The emergence and rapid development of Industrial Internetof Things (IIoT) lead to tremendous innovation in conditionmonitoring and control methods for electrical assets in recentyears. With advanced sensing and communication techniques,equipment operation status can be obtained instantly, prevent-ing system outages proactively from an early stage. However,existing IIoT is always developed by a single designer or

company, whose core technology and data are inaccessibleto other individuals. This type of IIoT operates in a close andrigid manner, with limited scale and functions. An architectureof open ecosystem for future IIoT is proposed in this paper.The open ecosystem depicts the structure of future IIoT, whichcan be defined as an open and scalable platform permitting freeinteractions between IIoT and various users. With the proposedecosystem, the users will be able to design IIoT functionsand applications according to specific demands, which largelyextends IIoT efficiency and ability. The open ecosystem canbe represented by a development environment open to de-signers, supported by both software and hardware facilities.The key feature of proposed future IIoT ecosystem lies inits openness: authorized users are treated as developers whocan design desirable functions or improve IIoT performanceby developing web or mobile applications. Accessible to allusers, the proposed structure leads to future open ecosystemof multifunctional IIoT with high flexibility, reliability andefficiency.

The proposed open ecosystem for future IIoT is comprisedof intelligent machines, servers, web & mobile applicationsand display technologies, as shown in Fig. 1. All componentsare interconnected by wireless communication tools (e.g. NB-IoT). Various kinds of sensors are deployed on key industrialcomponents to obtain comprehensive operation informationwhich is instantly transmitted to a cloud or on-premise server.These data are then stored in the server and can be pro-cessed by industrial or enterprise software to realize conditionmonitoring and fault prognostic for power devices. Unliketraditional IIoT working pattern, an IIoT-based developmentenvironment is provided and designed in the open ecosystem.Data in the server are accessible to all users and can be down-loaded freely. Developers are capable of designing web & mo-bile applications using developer toolkit for specific demands.The applications can be visualized by display technologies(e.g. desktop, big screen, mobile, virtual vision glass & otherwearable equipment, on-machine), and application outputs canbe delivered to a digital simulation system integrated in serversas commands. The digital simulator consists of multi-physicsmodels of power devices, which can emulate device operationunder normal, aged and fault conditions. System performancebased on current state and received commands is simulatedand the results are fed back to users for evaluation and adjust-ment. When multi-requirements are raised by different users,tradeoffs are made in iterative simulation procedure, and finaloperation commands chosen by the users are transmitted to thecontroller software services. The operation mode of industrialsystems can then be changed and intelligent operation andmaintenance are realized. Direct connection path can alsobe established between the controller software services andmobile apps to realize real time monitoring conveniently.

The salient feature of the proposed future IIoT architectureis its bidirectional openness, i.e. entire time-scale monitoringdata of industrial systems can be accessed by every individ-uals or enterprises, and user commands can be received andprocessed by the server. A unified development environmentenables IIoT users to design web or mobile applicationsaccording to different desired functions. User commands are

ZHANG et al.: OPEN ECOSYSTEM FOR FUTURE INDUSTRIAL INTERNET OF THINGS (IIOT): ARCHITECTURE AND APPLICATION 3

Server

Cloud&/or On-Premise

Software Platform

Core

Platform

Software

Services

Business-

Specific

Software

Services

Industrial+

Enterprise

Software

Controller

Software

Services

Direct

Connection to

Mobile Apps

Intelligent

Machines

Developer

Toolkit

Web&Mobile

Applications

Display

Technologies

Desktop

Big Screen

Mobile

Glasses&Other

Wearables

On-Machine

Fig. 1. Architecture of the open ecosystem for future IIoT.

generated based on obtained operation data, algorithm analysisand specific criteria formulated manually. The applicationcommands are then fed back to the server and may changeintelligent machine operation pattern after synthesis simulationand evaluation.

It should be noted that the digital simulator integrated inthe server forms the physical foundation of the proposedIIoT structure. The simulator contains complete multi-physicsmodel of power devices with electrical, mechanical, and ther-mal characteristics. Designed simulation algorithms have theability of computing and forecasting. The real time operationstatus of inspected machines is estimated based on uploadedmonitoring data collected by sensors, providing reference forindustrial monitoring schemes. More essentially, the simulatorcan realize system operation state forecasting both in short-time and long-time scale, according to present operationstate and user commands from client applications. Simulationresults are then fed back to users, forming a closed informationloop. Thresholds and forbidden operation area should beplanted in the simulator in advance, preventing fault demandsinjection and increasing system reliability. It can be observedthat the digital simulator is a critical component for futureIIoT, ensuring interactive and safe system manipulation.

To conclude this section, two typical applications of futureIIoT in power electronic devices and wind turbine variablepitch systems are elaborated briefly as follows. The switchingfrequency is an essential control factor for power electronicdevices which are widely utilized in power grids as interfacebetween transmission grid and high penetration distributiongenerators (DGs) [24]. By increasing converter switchingfrequency, harmonic components can be attenuated and powerquality can be improved. However, larger switching frequencyalso exerts extra stress on device insulation, which may causeaccelerated aging and threaten the normal operation of power

devices [25]. The selection of switching frequency becomesan essential issue which is difficult to solve by traditionalcontrol methods. Such problem also exists in wind turbinevariable pitch system where instantaneous adjustment of pitchangle for maximal power operation point tracking improvesturbine effectiveness, but the frequent operation of variablepitch system will in turn cause bearing accelerated agingby the same time [26], [27]. The proposed open ecosystemfor future IIoT provides perfect solutions for such situations.Based on the information collected by various kinds of sensors,comprehensive depictions of power electronic devices andwind turbine variable pitch system operation can be obtained.Control schemes can then be adjusted based on operationcondition in an optimal manner, ensuring system reliabilityand effectiveness. A compromise will be reached with powerelectronic devices and wind turbines operating under maximalefficiency and reliability. Besides, users of future IIoT canparticipate in power industry operation from web or mobileapplications and remote control can be realized. The demandsfrom users can be analyzed and processed by digital simulatorin the server, further realizing optimization of whole powersystem operation. The predicting ability of IIoT also providesguidance for industry assets maintenance, reducing operationcost and extending service life.

It can be observed that the ecosystem of future IIoT providesnovel and efficient solutions for applications in both powerelectronic devices and wind turbine variable pitch systemmaintenance. Above discussion has illustrated the broad ap-plication of future IIoT open ecosystem in power systems,leading to an innovative prospect for power device condi-tion monitoring and intelligent maintenance of high security,reliability and efficiency. The characteristics of future IIoTcan be concluded as follows: open ecosystem with interactionbetween users and industrial systems, intelligent control and


maintenance realized by remote software applications on userterminals, and optimal control scheme based on informationanalyzing and synthesis.

III. CORE TECHNOLOGIES FOR FUTURE OPEN IIOTECOSYSTEM

The implementation of open ecosystem for future IIoT relieson advanced sensing, communication and control techniques.Main core technologies for future open IIoT ecosystem areshown in Fig. 2.

BigData

Communi-cation

Sensor

ControlMainte-nance

Analysis

Open Ecosystem for

Future IIoT5G NB-IoT

Sensor Technology with

Effective and Predictive

Analysis Capability

Intelligent Control Method

Automatic Maintenance

Robotics Technology

Algorithm of Failure

Analysis & Prediction

Machine Learning

Big Data Platform

Cloud Computing

Fig. 2. Core technologies of the open ecosystem for future IIoT.

The openness nature of future IIoT raises challenges forrelevant technologies and reveals future developing trend atthe same time, which can be concluded as follows: fromparticular sensing to comprehensive sensing, from small-rangecommunication to wide-area communication, from closed BigData infrastructure to open and sharing Big Data service viaplatform on cloud, from fixed data analysis to interactivedigital simulation system capable of monitoring and predictingwith advanced machine learning algorithms, and from manualdevice maintenance to intelligent and automatic maintenance.Each core technology of future open IIoT ecosystem will bediscussed in detail in this section.

A. Sensors

Advanced sensing technique is a prerequisite for futureopen IIoT ecosystem. Nowadays, all kinds of sensors areutilized to improve the visibility of power assets, which mayinclude electrical sensors (e.g. voltage sensor, current sensor),mechanical sensors (e.g. vibration sensor, torque sensor), ther-mal sensors (e.g. thermocouple, thermal resistance sensor) andother sensors based on chemical or physical principles [28].The open ecosystem of future IIoT requires that the sensormeasurement data are open to every developer of the IIoTand can be adapted for various functions and operation de-mands. Therefore, unlike traditional IIoT where designatedsensors of certain types are equipped for specific functions,all types of sensors are deployed on the electric devices infuture IIoT to depict system operation state with maximalcomprehensiveness. All monitoring data are uploaded to theserver and measurement data from certain types of sensors

can be further downloaded and processed by web or mobileapplications according to user demands.

Sensors in future IIoT contain sensing module, commu-nication module and signal processing module. The sensingmodule transforms physical or chemical characteristics intoelectrical signals, which can be launched to the server bycommunication module. The signal processing module infuture IIoT sensors can be integrated in a system on chip(SoC) or microcontroller (MCU) [29]. This module performspre-process for the measurement data to improve data quality,and transforms the electrical signal to a standardized formatwith value and time stamping. The predicting capability is alsointegrated in the signal processing module where forecastingresults are obtained with simple algorithms. The predictingcapability is capable of sending alarms or taking immediateactions under urgent situations with blocked communicationpath or data attack, providing a backup protection scheme forelectrical assets.

Features of future IIoT sensor also lie in its long service life,low power consuming, small size and high precision. Industrialmonitoring sensors require long product life-cycles (around10 to 20 years) [3], during which the power requirement forsensors becomes a critical issue which may affect systemreliability. Energy harvesting solution with sensors powered bymachine vibration or electromagnetic energy emission is pro-posed to solve the dilemma [30]. Besides, implementation ofmicro-electro-mechanical system (MEMS) also lowers energyconsumption, reducing system size at the same time [31]. Withthe rapid development of sensing and micro manufacturingtechnologies, the sensors in future IIoT will be able to providereal-time high-precision monitoring data of power assets ina cost-effective manner, forming a hardware foundation forpower device intelligent condition monitoring and control.

B. Communication

Communication is an essential part in future IIoT openecosystem which interconnects every component and guar-antees reliable information exchanges. Due to the opennessnature of future IIoT, developers can obtain system oper-ation data and interact with servers and industrial systemsvia client terminal applications everywhere. Therefore, thetraditional connectivity using short-range communication tech-nologies will be replaced by low-power, wide-area network(LP-WAN) [32]. An open IIoT ecosystem also requires thatevery machine and user can exchange information in a simpleway, which means a unified communication standard shouldbe designed and authorized. Narrow-Band Internet of Things(NB-IoT) will become a preference as communication toolfor future IIoT [33]. Designed for sensor data transmissionin specified authorized spectrum, NB-IoT provides connec-tivity between machines and devices with ultra-low powerconsumption and wide area coverage. Besides, advancingcellular communication technologies (e.g. 4G and 5G) alsopresent an option for convenient interaction between IIoT andapplications installed on mobile phones.

C. Big Data

As demonstrated above, the proposed open ecosystem for


future IIoT demands that monitoring data from all kinds ofsensors are collected and stored, which means a huge amountof data will be generated and processed at all times. Thischaracteristic poses novel challenges for data storage andcomputing. Investigations on Big Data infrastructures anddata frames have been reported during the past decade tohandle problems with increasing data and information amount.Distributed storage and computing technologies such as edgecomputing are proposed where data are stored and processedin multiple nearby servers connected to the same internet,improving system efficiency and stability [34]. However, tra-ditional Big Data platforms operate in a fixed manner wherelimited interfaces and resources are accessible to the userswith little scalability. Users can only obtain information oruse applications predefined by technical developers, leadingto huge resource wasting in Big Data platform.

Considering the openness of future IIoT ecosystem, BigData servers including distributed servers will operate in aservice pattern, where data storage and computing resourcesare open to users as services. A development environmentwill be established in future IIoT where users can designapplications due to desired functions. Then web or mobileapplications can download required data from the Big Dataserver freely and register computation resources from the BigData platform for data analysis and processing. By turning BigData into flexible services, computation resources are savedand system scalability is largely extended in future IIoT.

D. Analysis

Data analysis is among the most essential technology inIIoT to extract useful information from a large amount of rawdata. Unlike traditional IIoT, data analysis in future IIoT openecosystem can be divided into two parts, which are analysis inuser terminal applications and digital simulator in servers. Asillustrated in above sections, users of future IIoT are capableof downloading power devices monitoring data and designingapplications based on their own demands. Therefore, specificalgorithms can be designed and data analysis can be realizedfrom user terminals. Data analysis is also implemented inservers in the form of digital simulators. The digital simu-lators contain multi-physical models of electrical assets to bemonitored and the algorithms with analyzing and predictingability. As proved in section II, the analysis module in serversis essential to maintain interactive and reliable IIoT systemoperation.

In future open IIoT ecosystem, measurement results fromdifferent types of sensors will be used, which may increasedata analysis difficulty due to data multimodality and hetero-geneity. Over past decades, machine learning algorithms havebeen widely utilized for data analysis in complex situations.Using monitoring data as training samples, fault detection andprognostics can be realized by neural networks or SVMs.With the development of Big Data infrastructure, deep learningneural networks of complicated structures (e.g. CNNs or otherdeep learning models) also become a suitable choice to realizedata classification and pattern recognition with high precisionfor future IIoT.

E. Control and Maintenance

The open ecosystem of future IIoT creates possibility forintelligent control and flexible maintenance for power assets.Based on the monitoring data collected and uploaded bysensors attached to electrical assets, operation condition ofindustrial systems can be obtained and control scheme can beadjusted instantaneously. The comprehensive monitoring dataof electrical assets provide possibility for optimal control ac-cording to information synthesis and optimization algorithms.Besides, the wide-area coverage of future IIoT also providesopportunity of coordinated control for energy assets in a broadspace range, leading to optimal operation of large industrialsystems.

As the users can design operation and maintenance schemefor power devices, which will be simulated and validated bydigital simulator on the server or web & mobile applications,the maintenance schedule can be arranged in a feasible andcost-effective way. The predicting ability supported by dataanalysis algorithms also leads to a variation from fault-basedmaintenance to condition-based maintenance. It can also beexpected that the normal operation of power grid will notbe interrupted by alternately shifted maintenance of electricalassets, further improving system reliability. With the devel-opment of robotics techniques, maintenance accomplished byrobots has become a preference for power devices operatingunder undesirable environment [35]. Due to the opennessnature of future IIoT where comprehensive sensing data andwide-area communication networks are presented, the naviga-tion precision of maintenance robot can be further improved,realizing system maintenance in a remote and intelligentmanner.

With above core technologies, an open ecosystem for futureIIoT can be constructed, leading to tremendous evolutionfor electrical assets condition monitoring and maintenance.A comparison of core technologies in traditional and futureIIoT is shown in Fig. 3. As a conclusion, the flexibility andscalability of future IIoT require comprehensive informationof system operation states with advanced sensing techniques.Besides, the openness of future IIoT highly relies on wide-area communication between different terminals. System ef-fectiveness is further guaranteed by big data framework andadvanced data analysis algorithms. Intelligent control andmaintenance can then be realized, which further improvessystem reliability and efficiency. Huge challenges are raisedfor above technologies, which have become urgent bottlenecksfor open ecosystem construction in future IIoT. It is delightfulto notice that relevant technologies are developing rapidly, andrealization of open ecosystem for IIoT can be expected in thenear future.

IV. AN EXAMPLE OF IIOT APPLICATION IN WIND FARMMAINTENANCE

Wind power is one of the most popular and promisingenergy resources due to the renewable and clean nature. Windenergy has been utilized worldwide to reduce environmentpollution and fossil fuel consumption. Reports show that thecumulative capacity of wind power is 89,077 MW in the


Comprehensive operation statedepiction with all kinds of

sensors

Specific kind of sensors for pre-determined IIoT functions

Small-range communicationbetween electrical assets and

operation center

Big data service provided on asingle server for certain

operation staff

Control and maintenance ofspecific electrical assets or

factories

Traditional IIoT

Algorithm designed andintegrated in advance for

certain functions

Wide-area communicationbetween electrical assets

and users

Big data service open to usersand designers

Ability to realize real-timeelectrical assets conditionmonitoring and prediction

Intelligent control andmaintenance of entire power

system

Open ecosystem for future IIoT

Advanced sensing techniques toobtain operation information

Wide-area communicationtechniques, unified standard

Cloud platform for data storingand computing, anti-attack

ability

Accurate electrical and physicalmechanism model for electrical

assets operation

Algorithm for entire power systemoptimal control, maintenance and

energy dispatch

Technology bottlenecks for future IIoT

Fig. 3. Core technologies comparison between traditional IIoT and future IIoT open ecosystem with existing technology bottlenecks.

USA, and this figure increases to 188,392 MW in China, bothinvestigated in 2017 [36]. However, the maintenance of windturbines has become a critical issue in wind energy generationand utilization. It is estimated that the maintenance cost ofmechanical failure can be up to 400 k dollars, which will bemore for off-shore wind turbines [37].

Nowadays, online monitoring of wind turbines is mostlyrealized by vibration sensors with a success rate of 50%, whichis far below power system reliability demands. Novel low-costand reliable online monitoring methods for mechanical failurein wind turbines are required to decrease the maintenanceand operation cost of wind-turbines, reducing the total costof electricity generation at the same time.

The open ecosystem of future IIoT provides innovativemethods for wind farm maintenance, as shown in Fig. 4.Under open IIoT structure in wind farms, all kinds of sensorsare used to collect wind turbine operation information andmonitoring data, which will be launched to the Big Data serverwith wide-area communication. The intelligent analysis systemintegrated in the server then realize condition monitoring andprognosis with designed algorithms. Combining wind turbineoperation condition and user demands, the intelligent analysissystem is able to provide an optimal maintenance scheme withimproved system efficiency and reduced maintenance cost.The intelligent system can then issue preventative mainte-nance instructions for maintenance crew who will carry outmaintenance based on system guidance. Besides, maintenancecommands can also be sent to the equipment control system,which realizes intelligent operation mode adjustment or systemmaintenance with automatic robots.

The open ecosystem of future IIoT also provides interac-

tive channels between maintenance staff, wind turbines andservers. Maintenance criteria can be designed by applicationsdeveloped on web and mobile terminals based on various userdemands. Once a maintenance scheme is designed, simulationsand evaluations will be realized by digital simulators in theserver, ensuring a reliable and cost-effective maintenanceschedule. The components of future open IIoT ecosystemapplied in wind farms will be discussed below.

The Intelligent analysis systemoptimizes efficiency and reduces operation & maintenance cost

Maintenance based onsystem guidance

Maintain data is sent to theequipment control system for

intelligent adjustment

The Intelligent system issues preventativemaintenanceinstructions

Interaction viaweb&mobileapplications

The self-monitoringequipment launches

sensor data

Fig. 4. Wind farm maintenance with open ecosystem in future IIoT.

A. Sensors Attached to Wind Turbines

Open ecosystem requires comprehensive depiction of windturbine operation state with all kinds of sensors. Some typicalsensors that can be attached to wind turbines for conditionmonitoring are listed as follows.


1) Acceleration SensorsVibration signals collected with acceleration sensors have

been widely used for condition monitoring and fault diagnosisin wind turbines [38]. Under abnormal operation situations,fault characteristics can be extracted from vibration signalby various algorithms both in time and frequency domain.Although acceleration sensor signal analysis is a mature mon-itoring method with industrial standard (ISO10816), the preci-sion of monitoring and fault diagnostics with single vibrationsignal analysis fails the demands for comprehensive and highaccuracy system condition monitoring and fault diagnosis.2) Electrical Sensors

Electrical signals have become a preference for wind tur-bine condition monitoring because the inspected signals canbe conveniently obtained with sensors. By placing voltageprobes and Rogowski coils on the wind turbine, voltage andcurrent signals can be collected and processed by designedalgorithms, realizing system condition monitoring in a non-intrusive manner.3) Strain Sensors

Stain sensors are capable of turning strain signals intoelectrical signals. Cracks in wind turbine tower and bladescan be detected by strain sensors [39]. FBG strain sensorshave already been utilized in wind turbine blade conditionmonitoring and usages in wind tower monitoring are alsoreported [40]. However, collected strain signals may be largelyinfluenced by the environment and working conditions, makingmonitoring with single sensor inappropriate.4) Temperature Sensors

Researches have shown that abnormal temperature risingcan be observed under system fault situations with large energydiffused as heat [41]. Temperature sensors can be installedon wind turbines to realize real-time monitoring of systemoperation state. However, measurement of temperature sensorsmay be largely influenced by environmental temperature orhumidity, deteriorating monitoring reliability.5) Oil Particle Counter

Oil particle counter can detect contamination in hydraulicand lubricating oil of wind turbines to realize conditionmonitoring in a continuous manner [42]. Although researcheshave proved the feasibility of using oil particle counter tomonitor wind turbine operation, different kinds of lubricatingoil require specific monitoring criteria with no widely ac-cepted standards. Besides, the monitoring range is also limited,mainly for bearing box.

Apart from the above sensors, acoustic sensors and torquesensors have also been used in wind turbine condition moni-toring [43], which can collect acoustic emission from systemabnormal vibration or measure torque fluctuation under faults,respectively. However, both sensors suffer from difficulty ininstallation and outside environmental disturbance. With therapid development of sensing technology, more sensors willbe designed for wind turbine condition monitoring.

Unlike traditional IIoT where specific type of sensor isdeployed on the electrical assets to collect certain kind ofinformation for condition monitoring, sensors of all kinds aslisted above will be attached to wind turbines in future IIoT. A

comprehensive depiction of wind turbine operation conditioncan be obtained, forming a physical foundation for further dataanalysis and maintenance arrangement.

B. Intelligent Wind Turbine in Future IIoT

Equipped with the above sensors, an intelligent wind tur-bine structure for future IIoT can be derived, as shown inFig. 5. Apart from traditional energy flow in wind turbines,which transforms wind energy into electrical energy throughmechanical energy, an information flow for intelligent moni-toring and maintenance is also included in future IIoT-basedwind turbines. The main characteristics of intelligent windturbine in future IIoT lies in the comprehensive depiction ofoperation state, integrated analyzing and predicting algorithmsand the ability of communication and intelligent controlledmaintenance.

It can be observed from Fig. 5 that all kinds of sensors areinstalled in the wind turbine and a comprehensive descriptionof operation state can be obtained. The collected measurementresults are then processed by algorithms both in time domainand in frequency domain. Artificial intelligence algorithms,even deep learning algorithms can be used to realized featureextraction and operation forecasting. The measurement datacan also be displayed on web and mobile devices by a dataacquisition system for real-time processing and monitoring bywind turbine maintenance crew.

In the open ecosystem of future IIoT, the intelligent windturbine operation monitoring data are also uploaded to a serveron a cloud platform for storage or further computing. Theusers of IIoT are able to download desired sensor measurementdata and design applications according to their own demands.Various maintenance schemes can be proposed by differentusers and simulation and validation can be achieved by digitalsimulator on the server. After iterative emulations, a finaloptimal maintenance scheme can be scheduled and realizedby manual maintenance crew or robots. The wind turbineoperation mode can also be adjusted by electrical basedcontrol system to realized wind turbine operation in the mosteffective manner. With the application of wide-area commu-nication network in future IIoT, the coordinated operationand maintenance of various power assets can be realized,further improving system reliability. It can be concluded thatfuture open IIoT has provided the possibility for intelligentwind turbines, with a more effective and efficient conditionmonitoring and maintenance scheme for wind farms.

The cost of developing IIoT open ecosystem in wind farmsis further estimated, as shown in Table I. The economicloss caused by wind turbine fault is shown in Table II as acomparison.

It can be observed that the installation cost of the proposedIIoT system per wind turbine is lower than $2500, far lessthan each item of the economic loss caused by wind turbinefaults. Besides, by embedding smartness in each wind turbine,cloud computing in future IIoT can be accomplished in an edgecomputation manner, which reduces computation cost from $0.65/hour to $ 0.2 ∼ 0.5/hour for different edge cluster nodenumber [46]. Based on comprehensive operation informationand accurate condition estimation, the proposed IIoT open


Fig. 5. The structure of intelligent wind turbines in future IIoT [44].

TABLE IMAIN COST IN IIOT OPEN ECOSYSTEM CONSTRUCTION PER EACH INTELLIGENT WIND TURBINE

Sensor installation cost Control system Server fee Total costAcceleration sensor Electrical sensor Strain sensor Temperature sensor$ 100 $ 30 $ 150 $ 20 ∼ $ 200 ∼ $ 500 < $ 2500

TABLE IIECONOMIC LOSS CAUSED BY WIND TURBINE FAULTS [45]

Cost for replacements of major components Scheduled maintenance cost System interruption LossGearbox Generator Transformer Blade$ 335,000 $ 168,000 $ 112,000 $ 224,000 $ 19,000 > $ 56,000

ecosystem can reduce electrical assets fault rate and avoidsystem interruptions, providing tremendous potential benefitsfor system reliable and safe operation.

C. Future Conception and Works to be Done

The above analysis has provided an insight of intelligentwind turbines in open ecosystem for future IIoT. With datafusion from multi-types of sensors, the maintenance of windfarms can be realized in a more efficient and effective way.Besides, the open ecosystem of future IIoT also provides anapproach for users to design applications on web or mobiles

due to their own demands. By developing advanced dataprocessing and forecasting algorithms, current failure diagnos-tics of power equipment can be promoted into future failureprognostics of power assets. With wide-area communicationtools and information exchanging, the ability can be furtherextended as failure prognostics on system. The optimized con-trol based online condition monitoring and device intelligentmaintenance can be realized as a final goal and future trendof IIoT aided power industry maintenance.

Some typical potential applications of future IIoT in powersystems are shown in Fig. 6 and listed as follows.


Hydroeletric

Dams

PV

Pannels

Wind

Farms

Power Flow

Information

Operation

Information

Control and Maintenance

Scheme AdjustmentOpen Ecosystem of Future IIoT User

Demands

Load

Information

Underground Cables

System Topology

Intelligent Reconstruction

Distributed

Generators and Load

Adjustment

Commercial

Areas

Industrial

Factories

Power

Plants

Step-up

Transformers

Transmission

Lines

Step-down

Transformers

Electrical

Vehicles

Smart

Homes

Fig. 6. Typical applications of future IIoT in power systems.

1) Optimal Control and Maintenance of Electrical AssetsIn future IIoT, coordinated and intelligent control of the

entire system can be realized. Electrical assets operation statecan be adjusted instantaneously according to electrical energyand user demands varying with time. Combining energy de-mands prediction of the power system and operation conditionprediction of the electrical assets, the optimal maintenancescheme can be manipulated, further improving the efficiencyand economic performance of energy systems.2) Intelligent System Topology Reconstruction

In the open ecosystem of future IIoT, the comprehensiveoperation states of transmission lines can be obtained basedon sensor measurements. Combining power flow informationand transmission lines state prediction, the topology of thepower system can be adjusted to ensure system reliableoperation. When an incipient fault on overhead lines or cablesis predicted, power system topology can be reconstructed toavoid probable fault and power supply interruption.3) Dynamic Distributed Generators and Load Adjustment

Open ecosystem of future IIoT provides possibility fordynamic adjustment of distributed generators and load, whichcan be realized by spot electricity pricing adjustment. Dueto the complete transparency of the power system, the spotelectricity pricing can be adjusted considering all participatingelectrical assets, which in turn promoting the efficiency of theentire system.4) Information & Communication Technologies

Open ecosystem of future IIoT demands wide-area com-munication between each participants of the power system.To ensure effective information exchange between differentelectrical assets, a unified communication standard will beestablished and uniform data format will be designed. Theefficiency of communication can be largely improved andcommunication cost can be reduced.

Some essential works still have to be done before futureIIoT becomes a reality, which include the implementation of

digital simulator, establishment of development environmentand manipulation of a unified standard. As analyzed above,a digital simulator is integrated in the server to ensure asecure interaction between electrical assets and users. Thedigital simulator has the ability of condition monitoring andoperation state predicting based on collected sensor data anduser demands. Therefore, comprehensive multi-physics modelsof electrical assets in all operation conditions should beproposed with high accuracy. More researches on power devicephysical, chemical, mechanical and electrical characteristicsunder various operation states are required. Besides, an IIoTbased development environment should be established whereusers can design web and mobile applications due to their owndemands, highly relying on the advancement of programmingand communication methods. Considering that future IIoT willinclude various power assets and users, unified standards needto be designed. Data launched by electrical sensors shouldbe of the same format for the convenience and efficiency offurther computing and processing. Other works on sensingprecision, analyzing algorithms, hardware and software designare also essential in the development of the open ecosystemfor future IIoT.

V. CONCLUSION

An open ecosystem for future IIoT is proposed in thispaper and its application in wind farm maintenance has beenanalyzed in detail. The key characteristic of the future IIoTecosystem lies in its bidirectional openness, which providesinteractive channels between electrical assets and users.

Advanced sensing and communication techniques underpinthe development of future IIoT. Unlike traditional IIoT work-ing pattern where most data and programs are inaccessible tousers, a development environment will be established in theopen ecosystem of future IIoT, with users treated as developerswho are capable of designing web and mobile applicationsbased on their own demands. The core technologies of future


IIoT are discussed in the paper, including comprehensivesensing techniques, wide-area communication tools, Big Dataas service, advanced data processing and forecasting algo-rithms and intelligent maintenance schedule accompanied bycondition-based control.

An application of future IIoT in wind farm maintenancehas been proposed and analyzed. Various kinds of sensors aredeployed on wind turbines to obtain a comprehensive depic-tion of system operation state. Integrated with advanced dataprocessing and predicting algorithms, intelligent wind turbinesin future IIoT are able to provide an optimal operation andmaintenance scheme, which can be adjusted by user demandsfrom designed applications. The open and interactive futureIIoT ecosystem can lead to efficient and effective electricalmaintenance in wind farms, providing an innovative insightfor future smart grids of high reliability.

REFERENCES

[1] E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund, “Indus-trial internet of things: challenges, opportunities, and direction,” IEEETransactions on Industrial Informatics, vol. 14, no. 11, pp. 4724–4734,Nov. 2018.

[2] S. F. Fang, L. D. Xu, Y. Q. Zhu, J. Ahati, H. Pei, J. W. Yan, and Z.H. Liu, “An integrated system for regional environmental monitoringand management based on internet of things,” IEEE Transactions onIndustrial Informatics, vol. 10, no. 2, pp. 1596–1605, May 2014.

[3] G. Bedi, G. K. Venayagamoorthy, R. Singh, R. R. Brooks, and K. C.Wang, “Review of Internet of Things (IoT) in electric power and energysystems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 847–870,Apr. 2018.

[4] M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, “A review on Internetof Things (IoT), Internet of Everything (IoE) and Internet of NanoThings (IoNT),” in Internet Technologies and Applications (ITA), 2015.

[5] G. Yang, L. Xie, M. M?ntysalo, X. L. Zhou, Z. B. Pang, L. D. Xu, S.Kao-Walter, Q. Chen, and L. R. Zheng, “A health-IoT platform basedon the integration of intelligent packaging, unobtrusive bio-sensor, andintelligent medicine box,” IEEE Transactions on Industrial Informatics,vol. 10, no. 4, pp. 2180–2191, Nov. 2014.

[6] C. F. Pasluosta, H. Gassner, J. Winkler, J. Klucken, and B. M. Eskofier,“An emerging era in the management of Parkinson’s disease: wearabletechnologies and the internet of things,” IEEE Journal of Biomedicaland Health Informatics, vol. 19, no. 6, pp. 1873–1881, Nov. 2015.

[7] J. Lin, W. Yu, N. Zhang, X. Y. Yang, H. L. Zhang, and W. Zhao, “Asurvey on internet of things: architecture, enabling technologies, securityand privacy, and applications,” IEEE Internet of Things Journal, vol. 4,no. 5, pp. 1125–1142, Oct. 2017.

[8] Z. Y. Zhou, C. X. Gao, C. Xu, Y. Zhang, S. Mumtaz, and J. Rodriguez,“Social big-data-based content dissemination in internet of vehicles,”IEEE Transactions on Industrial Informatics, nol. 14, no. 2, pp. 768–777, Feb. 2018.

[9] C. Perera, C. H. Liu, and S. Jayawardena, “The emerging internetof things marketplace from an industrial perspective: a survey,” IEEETransactions on Emerging Topics in Computing, vol. 3, no. 4, pp. 585–598, Dec. 2015.

[10] M. Aazam, S. Zeadally, and K. A. Harra, “Deploying fog computingin industrial internet of things and industry 4.0,” IEEE Transactions onIndustrial Informatics, vol. 14, no. 10, pp. 4674–4682, Oct. 2018.

[11] L. D. Xu, W. He, and S. C. Li, “Internet of things in industries: asurvey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4,pp. 2233–2243, Nov. 2014.

[12] M. T. Liu, F. R. Yu, Y. L. Teng, V. C. M. Leung, and M. Song,“Performance optimization for blockchain-enabled industrial Internet ofThings (IIoT) systems: a deep reinforcement learning approach,” IEEETransactions on Industrial Informatics, vol. 15, no. 6, pp. 3559–3570,Jun. 2019.

[13] J. Zhang, X. Feng, and Z. Liu, “A grid-based clustering algorithm viaload analysis for industrial internet of things,” IEEE Access, vol. 6, pp.13117–13128, Mar. 2018.

[14] Z. Y. Zhou, C. T. Zhang, C. Xu, F. Xiong, Y. Zhang, and T. Umer,“Energy-efficient industrial internet of UAVs for power line inspectionin smart grid,” IEEE Transactions on Industrial Informatics, vol. 14,no. 6, pp. 2705–2714, Jun. 2018.

[15] R. Usamentiaga, M. A. Fernandez, A. F. Villan, and J. L. Carus, “Tem-perature monitoring for electrical substations using infrared thermogra-phy: architecture for industrial internet of things,” IEEE Transactionson Industrial Informatics, vol. 14, no. 12, pp. 5667–5677, Dec. 2018.

[16] R. Chaudhary, G. S. Aujla, S. Garg, N. Kumar, and J. J. P. C. Rodrigues,“SDN-enabled multi-attribute-based secure communication for smartgrid in IIoT environment,” IEEE Transactions on Industrial Informatics,vol. 14, no. 6, pp. 2629–2640, Jun. 2018.

[17] X. M. Li, D. Li, J. F. Wan, C. L. Liu, and M. Imran, “Adaptivetransmission optimization in SDN-based industrial internet of thingswith edge computing,” IEEE Internet of Things Journal, vol. 5, no. 3,pp. 1351–1360, Jun. 2018.

[18] M. Marjani, F. Nasaruddin, A. Gani, A. Karim, I. A. T. Hashem, A. Sid-diqa, and I. Yaqoob, “Big IoT data analytics: architecture, opportunities,and open research challenges,” IEEE Access, vol. 5, pp. 5247–5261, Mar.2017.

[19] J. F. Wan, J. P. Li, M. Imran, D. Li, and Fazal-e-Amen, “A blockchain-based solution for enhancing security and privacy in smart factory,” IEEETransactions on Industrial Informatics, vol. 15, no. 6, pp. 3652–3660,Jun. 2019.

[20] J. Q. Huang, L. H. Kong, G. H. Chen, M. Y. Wu, X. Liu, and P. Zeng,“Towards secure industrial IoT: blockchain system with credit-basedconsensus mechanism,” IEEE Transactions on Industrial Informatics,vol. 15, no. 6, pp. 3680–3689, Jun. 2019.

[21] F. Samie, L. Bauer, and J. Henkel, “From cloud down to things: anoverview of machine learning in internet of things,” IEEE Internet ofThings Journal, vol. 6, no. 3, pp. 4921–4934, Jun. 2019.

[22] M. Mohammadi, A. Al-Fuqaha, M. Guizani, and J. K. Oh, “Semisu-pervised deep reinforcement learning in support of IoT and smart cityservices,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 624–635,Apr. 2018.

[23] K. K. R. Choo, S. Gritzalis, and J. H. Park, “Cryptographic solutionsfor industrial internet-of-things: research challenges and opportunities,”IEEE Transactions on Industrial Informatics, vol. 14, no. 8, pp. 3567–3569, Aug. 2018.

[24] M. V. Gururaj and N. P. Padhy, “A cost effective single architecture tooperate DC microgrid interfaced DFIG wind system during grid con-nected, fault and isolated conditions,” IEEE Transactions on IndustrialInformatics, to be published.

[25] M. S. Moonesan, S. H. Jayaram, and E. A. Cherney, “Time to failureof medium-voltage form-wound machine turn insulation stressed byunipolar square waves,” IEEE Transactions on Dielectrics and ElectricalInsulation, vol. 22, no. 6, pp. 3118–3125, Dec. 2015.

[26] H. Geng and G. Yang, “Output power control for variable-speed variable-pitch wind generation systems,” IEEE Transactions on Energy Conver-sion, vol. 25, no. 2, pp. 494–503, Jun. 2010.

[27] F. Z. Cheng, L. Y. Qu, W. Qiao, and L. W. Hao, “Enhanced particlefiltering for bearing remaining useful life prediction of wind turbinedrivetrain gearboxes,” IEEE Transactions on Industrial Electronics, vol.66, no. 6, pp. 4738–4748, Jun. 2019.

[28] Q. P. Chi, H. R. Yan, C. Zhang, Z. B. Pang, and L. D. Xu, “A reconfig-urable smart sensor interface for industrial WSN in IoT environment,”IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1417–1425, May 2014.

[29] M. Urbina, T. Acosta, J. Lazaro, A. Astarloa, and U. Bidarte, “Smartsensor: SoC architecture for the industrial internet of things,” IEEEInternet of Things Journal, vol. 6, no. 4, pp. 6567–6577, Aug. 2019.

[30] J. Y. Hu, J. Luo, Y. L. Zheng, and K. Q. Li, “Graphene-grid deploymentin energy harvesting cooperative wireless sensor networks for green IoT,”IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1820–1829, Mar. 2019.

[31] C. T. Chiang, “Design of a CMOS MEMS accelerometer used in IoTdevices for seismic detection,” IEEE Journal on Emerging and SelectedTopics in Circuits and Systems, vol. 8, no. 3, pp. 566–577, Sep. 2018.

[32] A. Ikpehai, B. Adebisi, K. M. Rabie, K. Anoh, R. E. Ande, M.Hammoudeh, H. Gacanin, and U. M. Mbanaso, “Low-power wide areanetwork technologies for internet-of-things: a comparative review,” IEEEInternet of Things Journal, vol. 6, no. 2, pp. 2225–2240, Apr. 2019.

[33] Y. B. Lin, H. C. Tseng, Y. W. Lin, and L. J. Chen, “NB-IoT talk: aservice platform for fast development of NB-IoT applications,” IEEEInternet of Things Journal, vol. 6, no. 1, pp. 928–939, Feb. 2019.


[34] H. M. Cai, B. Y. Xu, L. H. Jiang, and A. V. Vasilakos, “IoT-based bigdata storage systems in cloud computing: perspectives and challenges,”IEEE Internet of Things Journal, vol. 4, no. 1, pp. 75–87, Feb. 2017.

[35] C. Iwendi, M. A. Alqarni, J. H. Anajemba, A. S. Alfakeeh, Z. Y.Zhang, A. K. Bashir, “Robust navigational control of a two-wheeledself-balancing robot in a sensed environment,” IEEE Access, vol. 7, pp.82337–82348, Jun. 2019.

[36] S. Sawyer, L. M. Qiao, and L. Fried. (2018, May 4). GlobalWind Report—Annual Market Update 2017. [Online]. Availableonline: https://www.researchgate.net/publication/324966225 GLOBALWIND REPORT - Annual Market Update 2017.

[37] K. Alewine and W. Chen, “A review of electrical winding failures inwind turbine generators,” IEEE Electrical Insulation Magazine, vol. 28,no. 4, pp. 8–13, Jun. /Aug. 2012.

[38] S. Sheng, “Wind turbine gearbox condition monitoring round robinstudy-vibration analysis,” National Renewable Energy Lab. (NREL),Golden, CO, NREL/TP-5000-54530, 2012.

[39] J. Yoon, D. He, and B. Van Hecke, “On the use of a single piezoelectricstrain sensor for wind turbine planetary gearbox fault diagnosis,” IEEETransactions on Industrial Electronics, vol. 62, no. 10, pp. 6585–8593,Oct. 2015.

[40] C. H. Kin, I. Paek, and N. Yoo, “Monitoring of small wind turbine bladeusing FBG sensors,” in ICCAS 2010, 2010.

[41] P. Guo, D. Infield, and X. Y. Yang, “Wind turbine generator condition-monitoring using temperature trend analysis,” IEEE Transactions onSustainable Energy, vol. 3, no. 1, pp. 124–133, Jan. 2012.

[42] J. D. Zhu, J. Yoon, D. He, B. Qin, and E. Bechhoefer, “Onlinecondition monitoring and remaining useful life prediction of particlecontaminated lubrication oil,” in IEEE Conference on Prognostics andHealth Management (PHM), 2013.

[43] R. Pierzga and T. Boczar, “Analysis of low-frequency acoustic signalsemitted by low-power vertical axis wind turbine VAWT,” in Proceedingsof the 12th International Conference on Environment and ElectricalEngineering, 2013.

[44] P. J. Zhang and D. L. Lu, “A survey of condition monitoring and faultdiagnosis toward integrated O&M for wind turbines,” Energies, vol. 12,no. 14, pp. 2801, Jul. 2019.

[45] J. Nilsson and L. Bertling, “Maintenance management of wind powersystems using condition monitoring systems——life cycle cost analysisfor two case studies,” IEEE Transactions on Energy Conversion, vol.22, no. 1, pp. 223–229, Mar. 2007.

[46] D. Loghin, L. Ramapantulu, and Y. M. Teo, “On understanding time,energy and cost performance of wimpy heterogeneous systems for edgecomputing,” in 2017 IEEE International Conference on Edge Computing(EDGE), 2017.

Pinjia Zhang (M’10–SM’17) received the B.Eng.degree in Electrical Engineering from Tsinghua Uni-versity, Beijing, China, in 2006 and the Master’sand Ph.D. degrees in Electrical Engineering fromGeorgia Institute of Technology, Atlanta, GA, USA,in 2009 and 2010, respectively. From 2010 to 2015,he was with the Electrical Machines Laboratory,GE Global Research Center, Niskayuna, NY, USA.Since 2015, he has been with the Department ofElectrical Engineering, Tsinghua University as anAssociate Professor. His research interests include

condition monitoring, diagnostics and prognostics techniques for electricalassets. He has published over 80 papers in refereed journals and internationalconference proceedings, has over 40 patent fillings in the U.S. and worldwide.Dr. Zhang was the recipient of IAS Andrew W. Smith Outstanding YoungMember Achievement Award in 2018. He also received three best paperawards from the IEEE IAS and IES society.

Yang Wu received the B.S. degree in Electrical En-gineering from Tsinghua University, Beijing, China,in 2017, where she is currently working toward thePh.D. degree in Electrical Engineering. Her researchinterests include noninvasive measurement and con-dition monitoring of power systems and electricalassets.

Hongdong Zhu received the M.S. degree in ControlTheory and Control Engineering from Henan Uni-versity of Science and Technology, Luoyang, China,in 2001 and the Ph.D. degree in Control Theoryand Control Engineering from Shanghai Jiao TongUniversity, Shanghai, China in 2005.

From 2005 to 2007, he was a Lead Engineer inReal-time and power control Lab, Global ResearchCenter, General Electric, Shanghai. Currently, he ischief engineer of Envision Energy Company. He isleading the test validation department to validate

wind turbine performance and reliability through test-bench testing, prototypetesting, and vibration condition monitoring for 8000+ Envision turbines.

CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 6, NO. … · Index Terms—Industrial internet of...

Documents

Transcript of CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 6, NO. … · Index Terms—Industrial internet of...