Estrategia Integra Dade Manu Ten Cao

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Development and application of equipment maintenance

and safety integrity management system

Wang Qingfeng a,*, Liu Wenbin a, Zhong Xin b, Yang Jianfeng a, Yuan Qingbin c

a Engineering Research Center of Chemical Technology Safety of the Ministry of Education, Beijing University of Chemical Technology,

P.O. Box 130, 15 Beisanhuan East Road, Beijing 100029, Chinab The First Research Institute of the Ministry of Public Security, Beijing 100048, ChinacJinzhou Petrochemical Company, Jinzhou 121001, China

a r t i c l e i n f o

Article history:

Received 27 June 2010

Received in revised form

4 December 2010

Accepted 16 January 2011

Keywords:

MSI

Maintenance Indicator Decision-making

Maintenance Tasks Optimization

and Formulation

a b s t r a c t

Equipment management in process industry in China essentially belongs to the traditional breakdown

maintenance pattern, and the basic inspection/maintenance decision-making is weak. Equipment

inspection/maintenance tasks are mainly based on the empirical or qualitative method, and it lacks

identication and classication of critical equipment, so that maintenance resources cant be reasonably

allocated. Reliability, availability and safety of equipment are difcult to control and guarantee due to the

existing maintenance deciencies, maintenance surplus, potential danger and possible accidents. In

order to ensure stable production and reduce operation cost, equipment maintenance and safety

integrity management system (MSI) is established in this paper, which integrates ERP, MES, RBI, RCM, SIL

and PMIS together. MSI can provide dynamic risk rank data, predictive maintenance data and RAM

decision-making data, through which the personnel at all levels can grasp the risk state of equipment

timely and accurately and optimize maintenance schedules to support the decision-making. The result of

an engineering case shows that the system can improve reliability, availability, and safety, lower failure

frequency, decrease failure consequences and make full use of maintenance resources, thus achieving the

reasonable and positive result. 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Petrochemical production processes are complicated and highly

continuous,and the process medium is of high temperature,of high

pressure, inammable, explosive and toxic. Equipment in these

plants tends to become lager-scale and highly-automated, and if

certain unpredicted failure occurs, it may lead to huge economic

losses, environmental pollution and catastrophic safety accidents,

not only to themselves but also to the surroundings. Traditional

equipment maintenance and safety management thoughts bear

the characteristics of more inspection, more maintenance andbreakdown maintenance, which are still the dominance in

Chinese petrochemical enterprises. Unpredicted equipment fail-

ures and unplanned maintenance seem to happen more frequently

than ever before, which, substantially inuence the safety cost,

environmental cost and economic cost, making the reliability and

availability of equipment lower than expected. Within many large-

scale plant-based industries, maintenance cost can account as

much as 40% of the total operational budgets (Eti, Ogaji, & Probert,

2006), and therefore it is urgent and indispensable to improve

maintenance effectiveness.

Petrochemical plants around the world are trying to implement

reliability programs to improve plant safety while maintaining

equipment availability (Michel, & Mufeed, 2008).The benetsare so

evident that in several rening and petrochemical factories, main-

tenance budgets have been reduced by up to 50% (Rodney, 2001).

What reliability engineering is, how reliability models can be made

and what kind of data needs to collect have been discussed (Michel,

& Mufeed, 2008). An approach for the integration of RAMS and riskanalysis is developed as a guide in maintenance policies to reduce

the frequency of failures and maintenance costs (Eti et al., 2006).

Well-informeddecisions, basedon the sound reliabilityengineering

principle, have been used in industrial application of RAMmodeling

(Herder, van Luijk, & Bruijnooge, 2008) and the maintenance indi-

cators have been used to evaluate the effects of maintenance

programson performance and safety (Martorell, Sanchez, & Munoz,

1999). RIMAP methodology provides a guideline for making risk-

based decisions for maintenance and inspection planning, which

concentrates inspection activities on the key component which

bring increased safety and availability (Jovanovic, 2004). The* Corresponding author:Tel.: 86 010 64443058/8301, 86 18601128185 (mobile).

E-mail address: [email protected] (W. Qingfeng).

Contents lists available atScienceDirect

Journal of Loss Prevention in the Process Industries

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c om / l o c a t e / j l p

0950-4230/$e see front matter 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jlp.2011.01.008

Journal of Loss Prevention in the Process Industries 24 (2011) 321e332
mailto:[email protected]://www.sciencedirect.com/science/journal/09504230http://www.elsevier.com/locate/jlphttp://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://www.elsevier.com/locate/jlphttp://www.sciencedirect.com/science/journal/09504230mailto:[email protected]


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feasibility and advantages of integrating the safety management

system (SMS) and the equipment mechanical integrity (MI) by

means of RBI method has been demonstrated, and the essential

requirements forintegratingMI in SMSare discussedto focus on the

mandatory inspections and on critical components (Bragatto,Pittiglio, & Ansaldi, 2009). It can be seen from the above-

mentionedresearch that much literature researchhas been done on

reliability programs, but traditional technologies are onlyapplicable

to some certain stage or aspect of equipment management, the

existing information systems with different functions suchas ERP or

EAM, MES, Equipment Condition Monitoring System, risk assess-

ment tools (i.e. RCM, RBI and SIL), equipment maintenance/perfor-

mance evaluation system and reliability data collection system,

which are related to equipment management have almost been

ignored or isolated each other. Furthermore,there are fewer studies

on the combination of the above-mentioned technologies.

This paper aims to investigate a kind of MSI management

system to improve equipment reliability, availability and safety in

process industry. Based on the technologyof reliability engineering,risk-based management and professional management technology

are used to establish an intelligent maintenance indicator decision-

making model in MSI. This paper rst explains the principles of

equipment integrity management and the intelligent maintenance

indicator decision-making PDCACycling process. RBI, RCM, SIL, ERP,

MES, PMIS are integrated together to provide standard data struc-

ture in support of decision-making analysis and to eliminate

Information Island to share information for various experts in

different departments in process industry. The collection, storage,

loading and exchange of reliability and maintenance data are

complied with the unied standard, and the needed data for

quantitative risk analysis, fault prediction, fault diagnosis, mainte-

nance tasks optimization and formulation can all be achieved.

The next section outlines the framework of the equipmentintegrity management information system in process industry and

discusses the contents and features of equipment integrity manage-

ment. In Section3, we discuss at length some indicators of mainte-

nance decision-making and the intelligent equipment maintenance

indicator decision-making process. In Section4, we emphasize the

importance of MSI training for personnel at all levels. Section 5

illustrates and discusses an application case of MSI system. Conclu-

sions from building andutilizing MSI system is reported in Section 6.

2. Equipment integrity management in process industry

The intrinsic safety of a device relates to its design and quality,

and the safety production in process industry mostly relates to the

quality of installation and the maintenance of devices. For the

already-established equipment, the unreliability might be in

connection with design defects, incorrect manipulations, inadequate

maintenance or inability to predict failures that may occur during

operation (Eti, Ogaji, & Probert, 2007). Although statutory inspection

intervals enforced by legislations on pressure equipment and otherspecial equipments are implemented in China, but the inspection/

maintenance strategy is empirical, qualitative and arbitrary, and the

detection efciency of equipments defects and faults is very low.

Because of lacking the rst hand equipment health examination,

regulation inspection/maintenance tasks usually couldnt prevent

accidents or disasters resulting from equipments. Breakdown and

malfunction of equipment usually lead to biological and chemical

disasters. Statistics show that more than 40% of the disasters which

happened in petrochemical industry were caused by equipment

failure, so it is very important to ensure the integrity of equipment

(Jiang, & Li, 2007).It can beseen from Fig.1 that maintenance models

have experienced several phases, from breakdown maintenance,

preventive maintenance, predictive maintenance, risk-based main-

tenancetowards maintenanceand safety integrity management,andthere actually exists a close relationship between maintenance ef-

ciency and maintenance model. Reliability, availability, maintain-

ability and safety are the key indicators of maintenance efciency,

which are critical in optimizing maintenance model.

2.1. Contents of equipment integrity management

Equipment integrity management consists of two aspects:

management and technology. On one hand, it entails establishing

Abbreviations and Nomenclature

A availability

DCS Distribution Control System

CMMS Computerized Maintenance Management System

EAM Enterprise Asset Management

ERP Enterprise Resource Planning

ETA Event Tree Analysis

FMECA Failure Model Effect and Criticality Analysis

FTA Fault Tree Analysis

M maintainability

MES Manufacturing Execution System

MSI Maintenance and Safety Integrity Management System

MTBF Mean Time Between Failures

MTBO Mean Time Between Outage

MTTF Mean Time To Failure

MTTR Mean Time To Repair

NPP nuclear power plants

PDCA Plan-Do-Check-Adjustment

PM Plant Maintenance

PMIS Predictive Maintenance Information System

R reliability

RBI Risk-Based Inspection

RAMS Reliability, Availability, Maintainability and Safety

RCA Root Cause Analysis

RCM Reliability Centered Maintenance

SIL Safety Integrity Level

SOA Service Oriented Architecture

LCC Life Cycle Cost

U Utilization

ycneiciffEecnanetniaM

Maintenance and Safety

Integrity Management

Historic Development

Breakdown

Maintenance

Preventive

Maintenance

Predictive

Maintenance

Risk-based

Inspection/Maintenance

Fig. 1. Maintenance models and their corresponding maintenance efciency.

W. Qingfeng et al. / Journal of Loss Prevention in the Process Industries 24 (2011) 321e332322


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equipment integrity management system, and on the other hand,

on the basis of risk analysis, it utilizes technical measures in

combination with criterion management to ensure that critical

equipment can operate in stable condition.

There are mainly six elements in equipment integrity manage-

ment: identication and classication of critical equipment,

inspection and preventive maintenance, abnormality management,

quality ensuring, documentation of program and training, which,

not only involves the duty of management but also is concerned

with every staff in relation to the manufacturing process.

Equipment integrity management in process industry often

bears such features as below:

(1) Preventive maintenance is better than breakdown mainte-

nance, so that preventive maintenance plans and measures are

demanded for critical equipment;

(2) Risk rank of equipment is considered, and inspection/mainte-

nance resources should be transferred towards equipment of

higher risk rank;

(3) Process of the integrity management is dynamic and

continuous;

(4) Integrity management means that all equipments of one unit

or one system should be integrity characteristics and therequirements for integrity correlate with the risk rank of each

equipment;

(5) Integrity management runs through the entire process,

including design, manufacture, installation, utilization, main-

tenance and discard;

(6) Integrity management focuses on managing equipment

abnormality, which is often the precursor of failure;

(7) Integrity management requires that standard maintenance

procedures be made strictly and checked periodically for the

purpose of ensuring the working quality.

AsFig. 2 shows, equipment integrity management system can

be divided into four aspects: work execution and review, proactive

maintenance, risk-based management as well as MSI. Risk-basedmanagement which utilizes RBI, RCM and SIL evaluation tools to

identify and classify key equipments is the core content and the

technical support for the system. Risk-based evaluation can be used

to determine the risk rank of equipment, formulate optimal

maintenance tasks, allocate maintenance resources reasonably and

avoid maintenance deciencies/surplus, thus ensuring the reli-

ability of equipment. Preventive maintenance, predictive mainte-

nance and RCA are all proactive maintenance modes which are

applied by the integrity management. Predictive maintenance

information, risk rank of equipment and RAM indicators are the

basis to make inspection/maintenance strategies. In every stage of

the life cycle of equipment, purposeful preventive maintenance and

failure eradication plans are needed, especially for high-risk

equipment. During the work execution and review process, optimal

maintenance tasks are executed through EAM, CMMS or ERP

system, while failure data and maintenance data are recorded

according to certain standards. In the meantime, working tasks are

conrmed and optimized through professional management

programs in terms of lubrication management, operation

management, abnormality management (defect & fault manage-

ment) and archives management, thus ensuring the quality of the

workow.

Abnormality management, which can be divided into preven-

tive management and predictive management, is also important in

the integrity management system (Jiang, & Li, 2007). Some contents

of preventive management coincide with those of intrinsic safety

design. In order to avoid failures which are usually unobvious,

safety protection devices are set up during the reliability designprocess, which needs planned inspection, planned testing and

planned checking to formulate failure-pinpointing tasks. To achieve

the goal of intrinsic safety, the most important thing is to prevent

incipient failure in advance, and through self-diagnosis or self-

recovery, equipment can re-operate in an orderly and stable state

(Gao, & Yang, 2006). Effective preventive management can gener-

ally ensure the safety of individual equipment, but cant ensure the

integral safety of one unit or one system, while predictive

management can fulll this task. It utilizes predictive maintenance

technology in combination of vibration, temperature, pressure,

ow, liquid level, current, corrosion rate and other features to

perform incipient failure diagnosis by vibration analysis, thermo-

graph analysis, ultrasonic analysis and lubrication oil analysis. By

doing so, uncertainty of maintenance, failure frequency and failureconsequence can be reduced, thus minimizing maintenance cost

while improving operational safety (Ray, FIEAust, & CPEng., 2004).

2.2. Equipment integrity management information

systemof process industry

In order to improve utilization efciency of resources and

promote comprehensive management, many Chinese petrochem-

ical enterprises have made efforts to popularize ERP systems, by

which the logistics ow, capital ow and information ow are

integrated so that enterprise resources are effectively exploited.

However, the PM module of ERP system only utilizes twofunctions:

master data and maintenance worksheet of equipment, while the

preventive maintenance module and reliability managementmodule are not sufciently investigated or applied, and for this

reason the ERP system cant satisfy the need for integrity

management. Up to now, some enterprises have set up PMIS based

on condition monitoring technologies, MES based on production

management, EAM system based on the asset management and

CMMS based on maintenance management to promote the

equipment management level. However, the standards of reliability

data of different equipment are not consistent, so that information

is difcult to exchange, thus forming the information islands.

AsFig. 3shows, equipment integrity management refers to an

integral management system, in which there exist information

exchange and workow among every element and every hierarchy.

By using computer technology, web service technology and database

technology, we can establish the equipment integrity management

MSI

LCC SIL

RCM RBI RAM

Predictive

Maintenance

Work

Identification

& Prioritization

RCAPreventive

Maintenance

Lubrication

Management

Operation

Management

Defect/Fault

Management

Archives

Management

ERP-PMEAM

/CMMS

Maintenance & Safety

Integrity Management

Risk Based Management

Proactive Maintenance

Work Execution

& Review

Fig. 2. Equipment integrity management pyramid structure in process industry.

W. Qingfeng et al. / Journal of Loss Prevention in the Process Industries 24 (2011) 321e332 323


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system incorporating RCM, RBI,SIL and other risk evaluation tools on

the basis of SOA. Meanwhile, both online and ofine conditionmonitoring data from PMIS as well as process data from MES have

beenintegratedinto the system platform. Using OPC(OLE for Process

Control) and RFC (Remote Function Call) interface protocols, the data

communication between MSI and MES, ERP has realized separately

based on SOA technology. Equipment integrity management system

can minimize the routine inspection/maintenance work while not

causing negative impacts to equipment performance, product

quality, safety or environment. Process data, condition monitoring

data, historical inspection/maintenance data, key performance

indicator (e.g. RAM, LCC, MTBF, MTTR, failure frequency and failure

consequence) and dynamic risk evaluation data are all integrated to

perform comprehensive analysis through the unied data structure

and manemachine interface. Through the system platform,

personnel at all levels can master equipment operating status timelyand utilize dynamic data to make decisions for the purpose of

ensuring the operational safety and reliability (Deshpande & Modak,

2002).

Equipment integrity management in process industry is

a rigorous, scientic and normalized management model, and this

system is established with risk management as the core and

professional management as the mainline. Lubrication manage-

ment, operation management, abnormality management, archives

management, work identication and optimization management

can be gradually optimized via the system. Professional manage-

ment workow is designed based on the requirements to ensure

work quality, and the workow can be executed through the

system, so that the equipment management model can gradually

transform from function-oriented towards process-oriented. Based

on dynamic data analysis, an indicator decision-making mecha-

nism, which incorporates RAM, dynamic risk ranking and PMIS, hasbeen established to help assess the effectiveness of maintenance

measures. By monitoring the changes of indicators, maintenance

measures can be adjusted. It can be seen from Fig. 3 that the

equipment integrity management system in process industry is

a dynamic PDCA Deming cycle. The equipment fault diagnosis or

fault prediction information from PMIS can predict failure occurs

and failure trends, when the realtime signature strength exceeds

the threshold of alert level or alarm level of failure symptom

signature, fault can be diagnosed and the equipment residual life

can be calculated through fault degradation trend (Fig. 5). RAM is

a statistical and quantitative analysis indicator which reects

equipment reliability, availability and maintainability. Equipment

reliability or availability which falls short of expectation will be

identied and the weakness or failure will be eliminated. Risk valueresults from the combination of the consequence of failure and the

likelihood of failure, the higher risk rank, the more maintenance

resources (i.e. budget, personnel) should be exploited. With the

help of the indicator decision-making mechanism, basic manage-

ment of equipment can be improved, safety production can be

ensured, failure rate and failure consequences can be reduced,

equipment efciency and availability can be raised and mainte-

nance resource allocation can be more effectively and reasonably.

Predictive Maintenance Information System, which is based on

condition monitoring, web services, XML (Extensive Makeup

Language) signature, XML encryption, UML (Unied Modeling

Language) and other technology, is used to incorporate Condition

Monitoring System into equipment integrity management system

in order that it can realize performance monitoring, data collection,

RBI

Assessment Tool

RBI

Assessment Tool RCM

Assessment Tool

RCM

Assessment Tool SIL

Assessment Tool

SIL

Assessment Tool

RBI

Interface Module

RBI

Interface Module

MTBFMTBF

RAMRAM

RCARCA

Expert Revision

Module

Expert Revision

Module

Preventive

Maintenanc

PreventiveMaintenanc

Predictive

Maintenance

Predictive

Maintenance

Defect/Fault

Management

Defect/Fault

Management

Condition Based

Maintenance

Condition BasedMaintenance

Operation

Management

Operation

ManagementWork Identification

& Prioritization

Work Identification

& Prioritization Lubrication

Management

Lubrication

Management

RCM

Interface Module

RCM

Interface Module

SIL

Interface Module

SIL

Interface Module

Reliability Standard

Reference Data

Equipment Archival

Data

Inspection

/Maintenance

Work Program

Inspection

/Maintenance

History Database

PMPM

MMMM

COCO

PSPS

Master DataMaster Data

-SAP-ERP

SOA

LCCLCC

Plan

Do

Check Adjustment

RCARCA

RCARCA

MTTRMTTR

One-time changes

Maintenance

One-time changesMaintenance

Archieves

Management

Archieves

Management

Failure

Frequency

Failure

Frequency

Failure

Consequences

Failure

Consequences

Dynamic

Risk

Dynamic

Risk

MES

On-line

Condition

monitoringOff-line

Condition

monitoring

PMIS

Performance

monitoring

Fig. 3. MSI framework and roadmap of the intelligent equipment maintenance indicator decision-making in process industry.



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automatic failure diagnostics and failure prediction (Gao, 2001),

which can help determine inspection/maintenance measures, and

by doing so, failure diagnosis and maintenance decision-making

have been integrated. Condition-based maintenance techniques

utilized by PMIS include vibration analysis, infrared thermograph

analysis, lubrication analysis, tribology analysis, ultrasonic analysis,

motor current analysis, performance analysis, corrosion analysis

and so on. They aim to reduce unexpected failures via condition

monitoring and conduct remedial actions in case failure happens.

For rotating machines, rotor imbalance, shaft misalignment, shaft

cracking, blades rubbing, blade abnormality, blades stall/surge,

uidlm bearing wear, oil whirl, oil whip and other failures can be

automatically diagnosed or predicted through condition moni-

toring analysis. Different failures have different characteristic

signals, and it is assumed that the strength of the signals may

represent the severity of failures. Based on this assumption, the

optimal inspection/maintenance contents and periods can be

determined. Both automatic and manual failure diagnosis messages

underlie the maintenance decision-makings.

3. Intelligent equipment maintenance indicator

decision-making model

Martorell, Villanueva, and Carlos (2005)studied the application

of RAMS genetic algorithms-based information decision-making in

multi-objective optimization of maintenance and technical speci-

cations.Blanchard, Verna, and Peterson (1995)described how to

employ FMECA, FTA and ETA to perform RAMS and LCC risk anal-

ysis.Herder et al. (2008)investigated the industrial application of

RAM.Paul and Funkhouser (2007) studied equipment integrity and

risk analysis for reneries and chemical plants.Warburton, Strutt,

and Allsop (1998) presented a methodology for predicting char-

acteristics of mechanical-failures.Martorell et al. (1999)researched

the utilization of maintenance indicators that can evaluate the

performance of NPP and the effectiveness of maintenance

programs. No matter which method is selected for maintenance

decision-making, reliability prediction, risk evaluation and histor-ical failure data is necessary, while the lack of adequate reliability

data and maintenance data may lead to the difculty in failure

prediction and failure prevention using probability analysis, Wei-

bull plot, Monte Carlo simulation, Markov models, root cause

analysis and other reliability models that heavily depend on

probabilistic methods. MTBF, reliability and availability are all

related to maintenance decision-making criteria, of which the top-

down classication is established, including risk rank, failure

frequency, and safety level dened in RAM standard (EN50126-1,

2006). With RAM indicator and dynamic risk rank of equipment

taken into consideration, failure frequency and their corresponding

failure consequence can be reduced dramatically (Eti et al., 2007).

The biggest challenge for the China petrochemical enterprise to

establish Intelligent Maintenance Indicator Decision-making modelis the lack of historical failure data and maintenance data (Rodney,

2001). From the stance of the factory and equipment, this decision-

making model should make full use of dynamic risk rank indicator,

PMIS indicator and RAM indicator to evaluate the performance of

equipment, so that managers can formulate strategies and make

decisions by collecting, retrieving and analyzing all the informa-

tion. Measures should be focused more on high-risk equipment to

make the proactive maintenance task more efcient and effective.

Equipment failures are often caused by inadequate maintenance

and inability to predict incipient failure. PMIS can automatically

diagnose and predict failure and provide a foundation for decision-

making, such as recommendations for PM, spare parts and main-

tenance tools. Failure prediction and failure prevention is impor-

tant to ensure the stable operation of equipment, and thus

predictive maintenance can boost safety, quality and availability in

the process industrial plants (Carmen Carnero, 2006).

3.1. Reliability data and maintenance data for equipment

Equipment integrity management system has created a program

that collects reliability data and maintenance data through the

archive management workow. The probabilistic analysis, failure

consequence analysis, quantitative risk analysis, failure prediction,

failure prevention, maintenance task optimization and quantitative

indicators of performance monitoring heavily depend on reliability

data and maintenance data, so it will be the foundation of Main-

tenance Indicator Decision-making management to establish

standards for collection, recording, saving and exchanging of these

two types of data. In process industries, reliability data usually

includes the failure mode, failure cause, failure description, failure

position, failure consequences (e.g. safety consequence, economic

consequence, environmental consequences) and failure detection

method, while maintenance data mainly refers to the time when

potential failure is detected, when failure starts, when downtime

begins, when maintenance begins and when maintenance ends.

Both reliability data and maintenance data are often used in

probabilistic analysis, RCA, Weibull plot, Monte Carlo simulation,Markov model and so on to perform failure prediction, reliability

prediction and maintainability prediction.

As Fig. 4 shows, ti represents operation time, t0i represents

repair time, andTirepresents breakdown time. Provided that there

occurs N0 failures during operation and the equipment can

continue to be used as new one after repair. MTBF, MTTR and MTBO

can be calculated by Eqs.(1)e(3) respectively.

MTBF 1

N

XN0i 1

ti (1)

MTTR

1

NXN0

i 1 t0i (2)

MTBO 1

N

XN0i 0

Ti (3)

MTBF is related to availability, reliability and failure frequency,

which represents the number of accidents that occurred in a xed

interval of time. Failure consequence is studied from three aspects

such as the safety consequence, environmental consequence and

economic consequences, which is affected by failure consequence,

while the economic cost is proportional to MTBO.

3.2. RAM indicators

Petrochemical, chemical, rening, petroleum and other process

industrial plants are trying to implement risk-based maintenance

programs to improve safety, reliability and availability of the plants

(Warburton et al., 1998). RAM is one of the risk evaluation models

that are applied in MSI.Whether the implementation and utilization

of RAM indicator-based maintenance programs can be successful

Down

Time

Billing

Time

Maintenance

Start Time

Maintenance

Completion Time

Uptime

Ti

t0i

Down

Time

ti

Potential Failure

Detected TimeFault

End Time

P-F

Fault

Start Time

Fig. 4. Reliability data and maintenance data for equipment.



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heavily depends on the accuracy of the probabilistic models and

reliability data. Equipment failure mayimpact productivity and gross

prot (Warburton et al.,1998), which makes engineering solutions to

expense reduction, reliability enhancement and customer require-

ment satisfaction very important (Barringer, 2000).

3.2.1. Reliability indicator

Reliability, a probabilistic measure of the failure-free operation,

is the probability of the equipment functioning without failure

during a given time period under certain conditions (Kumar,

Klefsjo, & Kunar, 1992), which is often expressed as Eq.(4). It can

be improved by reducing failure frequency.

Rt exp

ti=MTBF

explti (4)

where l is a constant dened as the failure frequency.

Reliability determines whether the output of the plant is as

expected or whether the business can be protable, so it is of great

concern in terms of engineering application, and it helps determine

what andhow much maintenance shouldbe carried out. Equipment

with a long failure-free period can reduce accessories reserves and

maintenance cost. High-reliability can increase equipment avail-

ability while decreasing outrage time, maintenance cost and

secondary failure loss, and thus contribute huge benet for the

company. The key indicators which describe reliability include

MTBF, MTTF, mean life of components, failure frequency, maximum

number of failures permitted in a specic time-interval and so on.

3.2.2. Availability indicator

Availability is dened as the ability of equipment functioning

well during a denite period or even beyond it. It gives an indica-

tion of availableworking time during operation (Kumaret al.,1992),

and can be expressed as in Eq. (5).

Availability MTBF=MTBF MTTR (5)

Increasing failure-free time and decreasing downtime can enhance

availability, which can be converted into reliability and maintain-

ability requirements in terms of acceptable failure frequency and

outage hours.

3.2.3. Maintainability indicator

Maintainability is theability thatequipment canrestore to normal

functionin a speciedperiodof time orbeyondit (Kumar et al.,1992).

It correlates with design and installation quality. Maintainability

indicator can be usedto evaluate, ascertain and explain maintenance

programs and requirements. Maintenance project, personnel, orga-

nization, preparation and procedures all affect maintainability,

which is often expressed in Eq. (6). Designed maintenance proce-

dures and maintenance time are the baseline of maintainability, and

the keygure-of-merit for maintainability is MTTR.

Mt 1 exp

t0i=MTTR

(6)

The shorter MTTR is, the higher the maintainability will be. Three

main parameters: repair time (which is the function decided by

equipment design, and it is related to the training and skill of the

personnel in charge of maintenance), logistic time (i.e. time for

supplying parts) and administrative time (a function of operational

structure of the organization, standard maintenance procedure,

and maintenance quality assurance document) are concerned with

downtime.

High availability, reliability and maintainability and excellent

performance are characteristics of highly effective management,

and they are main indicators of lowering safety cost, environmental

cost and economic cost.

3.3. Equipment dynamic risk rank indicator

Theimportance of equipment may be represented by risk rank. In

general, the risk of equipment in process industry is studied in terms

of safety risk, environmental risk and economic risk, and it is con-cerned with the failure frequency, failure consequence, risk matrix

and risk criterion established according to management goals. Safety

risk rank is determined by safety consequence, failure frequency,

safety riskcriteria andsafety risk matrices; environmental riskrank is

determined by environmental consequence, failure frequency, envi-

ronmental risk criteria and environmental risk matrices; economic

risk rank is determined by economic consequence, failure frequency,

economic risk criteria and economic risk matrices. The criteria

which are related to reliability, availability and maintainability are

mainly dened by engineers, maintenance staffs, safety authorities.

Safety risk (Rs) is the product of safety probability of failure

(PoFs) and safety consequence of failure (CoFs), and it is calculated

by Eq.(7).

RS PoFS CoFS (7)

where PoFs is calculated from the failure frequency and CoFs is

specied by maintenance data from archive management module.

Environmental risk (RE) is the product of environmental prob-

ability of failure (PoFE) and environmental consequence of failure

(CoFs), and it is calculated by Eq. (8).

RE PoFE CoFE (8)

where PoFE is calculated from t failure frequency and CoFE is

specied by maintenance data from archive management module.

Economic risk (RC) is the product of economic probability of

failure mode (PoFC) and economic consequences of failure (CoFC),

and it is calculated by Eq. (9).

RC PoFC CoFC (9)

where PoFC is calculated from failure frequency and CoFC is speci-

ed by maintenance data from archive management module.

Economic cost mainly comes from production loss due to outage

time (Ti) and maintenance cost due to equipment failure (ti).

Equipment risk rank is dened by the highest risk rank of all risk

ranks corresponding to failure modes of equipment. Suppose the

risk rank of theith failure mode isRi, it is derived from Eq.(10).

Ri MaxRSi; REi; RCi (10)

Then the risk rankR is derived from Eq.(11).

R MaxRi; i 1; 2; 3; .; n (11)

In some cases, risk criteria are certain, so the main inuence

factors to dynamic risk changes are the failure frequency and failure

consequences, while failure frequency, also be called failure rate, is

usually more important. On one hand, the dynamic risk rank

indicator is an effective way of evaluating the previous risk rank

and inspection/maintenance task; on the other hand, it lays the

foundation for managers to revise management objectives and

establish the next risk evaluation task.

If the failure mode is identied, the risk is evaluated by

analyzing failure frequency, failure consequence and failure

detectability. If the risk is too high, efforts are needed either to

reduce the frequency and/or consequence, or to increase failure

detectability in order to make it possible to avoid or at least to

reduce the severity of the failure.



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management, abnormality management, operation manage-

ment, archives management, work identication and optimi-

zation. Inspection/maintenance tasks derived from risk

management are implemented through professional manage-

ment, which aims to ensure the operational quality and stan-

dard of inspection/maintenance management, and it is also

called the quality assurance workow procedures. Optimized

maintenance tasks (e.g. preventive tasks, predictive tasks, etc.)

derived from risk management are re-identied and conrmed

by maintenance experts. Various maintenance tasks which

include content, maintenance program, repair les and so on

are examined, conrmed and optimized by different adminis-

trative roles in the professional management workow.

Maintenance tasks are implemented by the PM module in ERP,

while reliability data and maintenance data are collected and

saved through PM02 maintenance orders and retrieved

through the interface between SOA and PM module. After

veried through archive management workow, the two types

of data are stored in the archive management module of MSI.

From maintenance strategies to maintenance orders, risk

Fig. 5. Failure symptom degradation trend can forecast equipment fault. Lines 1, 2, 3 and line 4 represent the rotor unbalance, the shaft misalignment and the bearing fault

degradation trend line respectively.



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management has been successfully combined with profes-

sional management.

(6) Dynamic risk evaluation. Dynamic risk evaluation is based on

risk rank that is determined by the highest risk rank of

equipment. Risk rank evaluation is conducted in terms of

safety, environment and economy, and it is affected by failure

frequency, failure consequence and risk criterion. If the risk

criterion is determined, the failure frequency and the failure

consequence, which are both key performance indicators of

reliability data, will determine risk rank. These two factors are

derived from the historical inspection/maintenance database

in the archive management module, and due to the changes of

historical maintenance data, risk rank will change accordingly

with those of failure frequency and failure consequence.

From RAM and risk analysis, equipment reliability or avail-

ability which falls short of expectation will be identied and the

weakness or failure will be eliminated. Equipment with more

failure frequency, long repair time or high degree of uncertainty

can be singled out. Maintainability analysis has been used

to evaluate the design and layout with respect to maintenanceprocedures and maintenance resources. Availability goals can be

converted into reliability and maintainability requirements by

means of acceptable failure frequency and outage hours for

equipment.

4. Training

MSI is a newequipment management model, and it involves risk

management and professional management such as RBI, RCM, SIL,

key device identication, key device classication, predictive

maintenance, condition monitoring, abnormality management,

data collection, data exchange, data storage, archive management,

lubrication management, operation management, failure identi-

cation, task optimization and so on. Personnel in charge of equip-

ment management have accustomed to traditional corrective

maintenance management culture. On the one hand, they want to

be able to jump out of the circle ofre-proof management mode,

and they wish equipment reliability, availability, maintainability

and safety can be promoted; on the other hand, due to the lack of

professional management knowledge and skills such as risk

management, reliability data collection, predictive maintenance,

probability analysis and reliability prediction, it is very difcult for

them to accept, master and apply novel equipment management

models. To make personnel at all levels understand and master MSI

better, training is very important, and the training content for using

MSI primarily include as follows:

(1) (Probabilistic) risk assessment technology, such as RCM, RBI,

SIL evaluation, Risk-based maintenance, probability analysis,

RAM statistical analysis and reliability prediction.

(2) The collection and exchange of reliability and maintenance

data for equipment technology, which involves data collection,

data exchange, data storage and archive management.

(3) Predictive maintenance technology, such as the fault symptom

signals detection, fault trend analysis & prediction, failure

diagnosis and so on.

(4) Intelligent equipment maintenance indicator decision-making

technology and the inspection/maintenance tasks formulating

technology.

(5) Equipment maintenance and safety integrity management

technology, such as identication and classication of critical

equipment, inspection and preventive maintenance, abnor-

mality management and so on.

International experiences show that the key factors affecting the

successful transition to a more risk-informed approach include rm

support from both the manager and the engineers as well as

education and training for engineers, operators and maintenance

staffs (Andrew, & Toshihiro, 2007). From the application point of

view, the transition involves reform in management programs and

management culture, which need to promote and establish

equipment management system, management procedures and

management culture in relation to MSI. The reform involves

working model and working skills. The popularization and appli-cation of MSI cant be nished at once, but needs continuous and

industrious improvement. Without the support from top leaders,

managers at all levels and staffs, the best equipment maintenance

and safety management decision-making model will fail (Jesus,

Jose, & Felix, 20 03).

Fig. 6. Inspection and maintenance task package formulating process, maintenance strategy derives from the intelligent equipment maintenance indicator decision-making model:

equipment dynamic risk rank indicator (2 M), RAM indicators (4 M), predictive maintenance indicator (5 M).

Table 1

Maintenance resource allocation proportion: optimizing maintenance model (using MSI) compared with traditional model (unused MSI).

Maintenance mode Breakdown

maintenance (%)

Preventive

maintenance (%)

Predictive

maintenance (%)

Risk-based inspection/maintenance MSI

Traditional 67 32 1 Unused Unused

Optimized 37 30 33 Being Applied Being Applied



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5. Applications case

In this study, MSI is developed in support of Jinzhou Petro-chemical Company, which aims to investigate a kind of mainte-

nance and safety management model that can help petroleum,

chemical, petrochemical and gas plants to improve reliability,

availability and safety for equipment as well as promoting the

equipment information management level. Using real-time data-

base, web service and SOA technology, a data framework is devel-

oped to provide a unied data structure and manemachine

interface. Meanwhile, this framework is integrated with process

data, condition monitoring data, historical inspection/maintenance

data, historical failure data and dynamic risk evaluation data to

support prediction and comprehensive analysis. As a result,

personnel at all levels can grasp equipment condition timely and

accurately in order to make maintenance and safety management

decision-making more effective and focused.The Jinzhou Petrochemical Company is a traditional rening and

chemical plant with a history of more than 70 years, breakdown

maintenance and preventive maintenance account for 67% and 32%

respectively, and predictive maintenance accounts for no more

than 1%. CMMS, MES and ERP have all been established and applied

in the plant, but they are isolated from each other and thus forming

Information Island due to the lack of unied interface and standard

reliability and maintenance data for exchange. Due to the lack of

identication and classication of critical equipment, it is very

difcult to rational allocation of maintenance resources only based

on empirical and qualitative approach. There exists some certain

maintenance deciency, maintenance surplus, high security hidden

danger and too many accidents. The foremost tricky problem is the

lack of historical data. Without them, we cant establish a quanti-

tative risk rank method.

Firstly, we established MSI system based on SOA as shown in

Fig. 3, which realized seamless connection in all function modules

and systems such as the risk evaluation tool, predictive mainte-

nance module, archive management module, operation manage-

ment module, lubrication management module, abnormality

management module, work identication and prioritization

module, MES and ERP system. An information collection and storage

system that can provide needed data for analysis is essential.

Secondly, data collection, data exchange and data storage

standards of both reliability data and maintenance data are estab-

lished according to ISO 14224:1999, so are the failure classicationand failure coding standards, making interconnection among all

function modules possible. Reliability data, maintenance data,

process data, dynamic monitoring data and archive data may be

retrieved from ERP, MES and PMIS, and quantitative risk rank

evaluation is established.

Thirdly, RAM indicator model, predictive maintenance indicator

model and equipment dynamic risk ranking indicator model are

established separately in support of maintenance decision-making.

After more than 1 years operation, failure modes, failure

frequency, failure consequence, MTBF and other reliability and

maintenance data for equipment are stored in the system, and

these data provide the data source for dynamic quantitative risk

evaluation and maintenance decision-making. At the same time,

failure frequency and failure consequences reduced, and mainte-nance resource allocation is optimized.

Table 1 shows that maintenance management mode and

resources allocation has experienced a dramatically change after

Table 2

Total rotating equipments risk rank evaluation (RCM) report for the Jinzhou Petro-

chemical Company.

Name

code

Equipment

category

Equipment

quantity

Risk level distribution

High (%) Medium (%) Low (%)

1 Reciprocating Compressor 41 41.50 58.50 0

2 Centrifugal Compressor 28 100 0 0

3 Screw Compressor 24 25 75 0

4 Centrifugal Pump 1692 2.80 32.50 63.705 Reciprocating Pump 129 0 27.20 72.80

6 Gear Pump 54 0 0.00 100

7 Screw Pump 20 0 30 70

8 Liquid Ring Pump 12 0 0.00 100

9 Roots Blower 11 0 0.00 100

10 Centrifugal Fan 122 0 0.00 100

11 Axial Flow Fan 225 0 19.10 80.90

12 Steam Turbine 8 100 0.00 0

13 Flue Gas Turbine 3 100 0.00 0

14 Generator 7 100 0.00 0

15 Others 426 0 0 100

Failure

Freq

uency

Recipro

cating

Compr..

.

Centrif

ugalC

ompre

ssor

Screw

Comp

ressor

Centrifu

galPum

p

Recipro

cating

Pump

GearPu

mp

Screw

Pump

Liquid

RingP

ump

RootsB

lower

Centrif

ugalFa

n

Axial

FlowF

an

Steam

Turbin

e

FlueG

asTurb

ine

Genera

tor

Equipment Category

Unused MSI

Using MSI

Fig. 7. Failure frequency comparative analysis report for typical rotating equipments (using MSI compared with unused MSI).



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exploiting MSI. Risk-based inspection/maintenance is introduced

and applied in Jinzhou Petrochemical Company. Breakdown

maintenance resource expense of utilizing MSI compared with

traditional model has decreased 30%, and the predictive mainte-nance resource cost has increased 32% accounts for 33%.

Table 2describes the RCM evaluation results, the risk has been

classied into three rank levels (High, Medium and Low) according

to the quantitative analysis and calculation of risk evaluation, and

risk level distribution varies with equipment category. All centrif-

ugal compressors, steam turbines, ue gas turbines and generators

are high-risk equipments. And the high-risk equipments propor-

tion of reciprocating compressors, screw compressors and centrif-

ugal pumps account for 41.5%, 25% and 2.8% respectively.

Risk level is determined by equipment failure frequency, failure

consequence, risk matrix and risk criterion, and the best way to

lower risk of equipment is to execute inspection/maintenance tasks

formulated on the basis of risk evaluation. It can be seen from Fig. 7,

the failure numbers per year for reciprocating compressors,centrifugal compressors, steam turbines, ue gas turbines, gener-

ators and other high-risk equipments have reduced signicantly.

Table 3 demonstrates that the utilization MSI has a positive

effect on improving equipment reliability, availability and utiliza-

tion compared with traditional maintenance model (unused MSI),

and this can reduce the safety accidents which result from equip-

ment failure.

Predictive maintenance decision-making indicator of MSI plays

an important role on formulating optimum strategy that antici-

pates, avoids and eliminates problems and maintenance. Alongwith

RCM, RBI and SIL evaluation tools, PMISproveseffectively to identify

and eliminate defects, minimize and avoid failures, minimize

breakdown maintenance and maximize predictive maintenance.

6. Conclusions

This study emphasizes the importance of the integrating risk

management with professional management, and investigates the

necessity of applying MSI in process industry, which can guarantee

the failure-free operation of equipment in Jinzhou Petrochemical

Company. The application results suggest that the established RAM

indicator model, predictive maintenance indicator model and

dynamic risk rank indicator model should be considered in main-

tenance decision-making and maintenance planning in order to

increase reliability, availability, maintainability and safety of

equipment. In order to ensure the efciency and effectiveness of

inspection/maintenance, some professional management work-

ow such as lubrication management, abnormality management as

well as work identication and prioritization have been set up.

Based on principles of equipment integrity management in process

industry, MSI has realized PDCA Cycle: the formulation of the

inspection/maintenance program (Plan), implementation ofinspection/maintenance tasks (Do), performance check (Check)

and information feedback (Adjustment).

Maintenance standards, reliability data and maintenance data

are insufcient or even unavailable, while data exchange standards

for data collection and data exchange are not unied. Many

equipment management systems such as ERP, CMMS, EAM and

MES are isolated from each other, thus forming Information Island.

Enterprise management usually lacks comprehensive skills in

terms of risk management, probabilistic analysis, predictive main-

tenance and so on, and this hinders the successful application of

MSI. Engineering practice shows that support from the manage-

ment, improvement of equipment integrity management system,

promotion of management procedure as well as sustained training

and education are critical to the successful application of MSI. Thepilot application of this system in Jinzhou Petrochemical Company

shows that MSI can be built on the basis of traditional equipment

management model and risk evaluation together. RAM indicator

model, predictive maintenance indicator model and dynamic risk

rank indicator model are established to support maintenance

decision-making, which is actually benecial to the improvement

of reliability, availability, maintainability and safety of equipment,

and it can help optimize the maintenance resource scheme.

Acknowledgements

The authors would like to acknowledge the support of the

National Key Technology R&D Program (Approved Grant No.

2006BAK02B02) and the Scientic Research Foundation of Grad-uate School of Beijing University of Chemical Technology innova-

tion (Approved Grant No. 09Me003).

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Table 3

The positiveeffect of application MSI on improving equipment reliability (during 30 days), availability and utilization report for typical rotating equipments (Values in thetable

represent means in accordance with different equipment category.).

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