Enhanced System Reliability with Intelligent Sensors,...

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Enhanced System Reliability with

Intelligent Sensors, and Robust

Web-Enabled Technology

Bhanu Srilla, MS, CESCP, CMRP, CRL

Director of Product Marketing

Grace Engineered Products, Inc.

#DATACENTERWORLD | DATACENTERWORLD.COM

Overview of the Maintenance Spectrum

How to optimize a maintenance budget

Equipment Criticality, Prioritization and ROI Justification

IIoT Predictive Maintenance Roadmap

Deployment of sensors and web-enabled technology in asset management

Cybersecurity & Implementation challenges

Q&A and wrap up

Agenda


Electrical Engineering and Manufacturing system background

Power Generation, distribution Industry

Backup Power systems (ANSI & IEC Applications)

Design and build of IEC 62271 Metal Clad switchgear

Member of UL Standards Technical Panel (508,508A,61010 and 1436)

Speaker Background

3


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive

The Maintenance Spectrum

There are many approaches to maintaining equipment No one-size-fits-all solution Every situation is different

However, the goal of any maintenance program is: Optimal Maintenance Budget Allocation


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive


What is a Run-To-Failure Maintenance Strategy?Wait for a machine or process to misbehave, then either fix or replace

When is Run-To-Failure Maintenance Optimal?Cost of downtime is less than cost of any maintenance activityExample: Light bulbs


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive


What is a Schedule-Based Maintenance Strategy?Equipment is maintained or replaced on a pre-determined intervalTiming is often based on manufacturer's recommendations

When is Schedule-Based Maintenance Optimal?When failure mechanism is well understood and the cost of replacement is relatively lowExamples: Smoke detectors, lubrication systems


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive


What is a Risk-Based Maintenance Strategy?An assessment is done to list all possible failures by cost and probabilitySchedule-based maintenance is then prioritized based on this risk matrix

When is Risk-Based Maintenance Optimal?When failure mechanism is well understood, conditions are relatively constant, and the assumption of some risk is acceptableExamples: Road repair, plant-wide prioritization


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive


What is a Condition-Based Maintenance Strategy?Equipment is continually monitored to determine real-time performance levelRisk-matrices are updated with quantitative failure probability dataNOTE: Often called predictive, but no predictions are made

When is Condition-Based Maintenance Optimal?When equipment condition can vary and when downtime is expensiveExamples: Gas tank status, motor vibration status


Run-to-Failure

Schedule-Based

Risk-Based

Condition-Based

Predictive


What is a Predictive Maintenance Strategy?Data-driven and/or physics-based models are used to estimate time-to-failureAnomalous behavior is detected and used to pre-empt unexpected failures

When is Predictive Maintenance Optimal?When downtime is very expensive and redundancy is not feasibleExample: Gas tank miles to empty, motor vibration trending/anomaly detection



10

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Probability of Asset Failure

Ass

et M

ain

ten

ance

Co

st

1. Run-To-Failure

2. Scheduled

Maintenance

3. Risk-Based

Maintenance

4. Condition-Based

Maintenance

5. Predictive

Maintenance

Perspective: Cost of Maintenance

RTFSBM

RBM

CBMPM



11

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Ass

et M

ain

ten

ance

Co

st

Planned Replacement(Materials, Labor, Shipping)

Unplanned Replacement(Materials, Overtime, Expedite)

Up to 4X Planned Replacement

Unplanned Downtime(Materials, Overtime, Expedite)

>100X Unplanned Replacement

Simple Replacement (Light Bulb)

Perspective: Cost of Failure



12

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Ass

et M

ain

ten

ance

Co

st

Optimal Strategy:

When

Expected Cost of Failure

Exceeds

Cost of Maintenance

RTF

SBM

RBM

CBM


Infant Mortality Failures

during 1-2 years of

installation

End of life failures (Wear

out) (20+ Years)

80% of the failures are

stress related during

useful life

Bathtub / Reliability Curve


P-F Interval

Eq

uip

me

nt

Co

nd

itio

n

Time towards failure

F0

F1

Early warning signs

Functional Failure

F= Failure

P

P1

P2

P3

P0

P4


Eq

uip

me

nt

Co

nd

itio

n


F0

F1

Early warning signs

Potential Failures

P0= Vibration changes

P1= Debris Oil

P2 = Thermography

P3 = Preventive Maint

P4 = Audible Noise

Functional Failure

F= Failure

P

P1

P2

P3

P0

P4

P-F Interval

Pump Motor Application


P-F Interval

Pump Motor Application


F0

F1

Eq

uip

me

nt

Co

nd

itio

n

Early Detection

1-9 Months

Functional Failure

F= Failure

P

P1

P2

P3

P0

P4

Preventive Maint Reactive Maint

3- 12 Weeks 1-5 days

Potential Failures

P0= Vibration changes

P1= Debris Oil

P2 = Thermography

P3 = Preventive Maint

P4 = Audible Noise


1. Need to quantify risk across plant

• Probability of asset failure

• Using historical or manufacturer provided data

• Costs associated with asset failure

• Replace & repair costs

• Expedite costs

• Downtime costs (from production losses)

2. Need to assess cost of maintenance activities across plant

• Labor Sensors, Data Acquisition, Alerting, Analytics

Justifying a Maintenance Budget


Justifying ROI with Predictive tools: Where’s the Money?

Justifying a Maintenance Budget


Where should I apply IIoT in my facility?

IIoT Applicatio

ns

Predictive & Remote

Maint.Plant

Safety & Security Systems

Optimizing Energy

ConsumptionOzone,

Gas and Temp

Monitoring

Production

Equipment

monitoring

Industrial Heating,

Cooling & ventilatio

n


Traditional Predictive Maintenance including vibration monitoring, oil residue analysis and

thermal imaging still dominate.

Manual statistical modelling such as Excel has not been replaced by more advanced

technologies.

O&M pros expect that Automated systems for equipment failure reporting and Repair

scheduling will be adopted in next 5 Years

O&M Personnel are less enthused about IIoT than senior management

Skill shortage, Big data scientists and lack of understanding of ML and AR

Most professionals agree that IIoT improves OEE

Real-time Data and Analysis will allow better decision making

IIoT Maintenance Research Study


Improve Productivity

Reduce Downtime

Reduce Maintenance Costs

Improve Worker Safety

Perceived Benefits of IIoT

Executive ManagementMaintenance Crew/ Managers

Improve Operational Efficiency

Maximize asset utilization

Reduce asset life cycle cost

Scalability

Creating new business opportunities


IIoT Current State

12%

23%

33%

44%51%

0%

10%

20%

30%

40%

50%

60%

MachineLearning forPredictiveAnalytics

Advancedstatistical

modelling forPredictiveAnalytics

Rules-basedSCADA systems

(SPC, controllimits)

Manualstatistical

modelling forPredictive

Analytics (e.g.excel)

Traditionalmethods like

vibrationmonitoring, oilresidue analysis

or thermalimaging.


Arc Flash Incidents

5000A busway feeder, 10Ft, 750 lbs. over

2000 ft. installed MCC Bucket


Power Monitoring

Voltage, Current, Frequency

Phase Loss, Leakage

currents

Load profiles

Efficiency

Winding temp & Skin temp

Space Heaters

Application Scenario – Electric Motor/Pump

Vibration

Bearing Temp

Mechanical Torque

Soft footing

Flow

Strain on

couplings/Manifolds


Electrical (kVA & kVAR)

Power Monitoring

Load sharing and load management

Breaker /transfer switch status

Transfer switch and synchronizing

Exhaust Temp and particulate matter

Winding temp & Skin temp

Space Heaters

Exercise Data

Application Scenario – Backup Generator

Active Power (kW)Fuel level and

consumption

Lube oil level, pressure

Engine efficiency

Air flow

Run Hours

Differential Pressure

Cylinder Head and Manifold temp

Exhaust Temp and particulate

matter

Coupling alignment

Vibration


MCC/Switchgear Applications

26

Hotspot monitoring for busbar splices, joints

Ambient bucket measurements (ΔT)

CT, Rogowski Coil or Power Monitor

Create detailed analytic profile to predict

failures

Energy Monitoring

Load connections


Sensors and Hardware


Predictive Maintenance

28

M1

M2

M3

M4

80

2.1

5.4

(Zi

gBee

)

Data StorageVisualization

Analysis/Alerts

LTE (Cellular) Or 802.11 (WiFi)Ethernet/IP or Modbus to

Control/SCADA

Plant Manager

Maintenance Lead

Reliability Engineer

Joe

WebEmailTextWeb

EmailText


Remediation Instructions


Cybersecurity and Data Security

Job threat to Personnel

Conflicting priorities

Training and Scalability

Ability to integrate legacy equipment

Data Integration

Lack of skills

Barriers to IIoT Adoption


50 Billion connected devices by

2020

83% of Organizations think

Cyberattacks are one of the 3 Biggest

threats

NIST published ~90,000 known

vulnerabilities

Cybersecurity


70% of the IoT devices are vulnerable

to attack

66% of networks will have experienced

an IoT security breach

Average total cost of security breach is

$4 Million

28% to 47% of organizations

experienced IoT related security

breach

Cyber Threat in Numbers


• Internal and External sources

• Easy to find a Target https://www.shodan.io/

• Project SHINE

• Industrial device search engines ( Ex: SHODAN)

• The SHODAN search engine works by searching for commonly used TCP/UDP port

numbers

• Web, Telnet, SNMP and FTP are some of the more common ones

• Logs of the response on these ports is saved in a searchable database

• Try searching “OpenSSL”, “GNU”, or “NTPD” or industrial vendor’s names

Where does these attacks come from?

https://www.shodan.io/


• https://ics-cert.us-cert.gov/advisories

• Latest vulnerability patch is not always Updated for ICS

Industrial Control Systems Vulnerability (ICS)

https://ics-cert.us-cert.gov/advisories


• https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-82.pdf

NIST Published Final ICS Cybersecurity Guidelines

https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-82.pdf


Industrial Control Systems Standards

IEC 62443

UL 2900-2-2


Cloud Vs. On-Premise

Cloud On-Premise

Data Hosted on vendor’s servers and accesses through web browser, Apps

Data Hosted locally on company’s own servers and accessed through local UI

Pay as you go, per user, per month, per node etc. High upfront investment for hardware, software, licensing, tech support

Run many applications and provide deeper analytics and data insights

Limited analytics capability, rely on company’s resources

Easy access from any device, any computer Difficult access through firewalls and network security

OPEX CAPEX

Access to information through internet that could pose some security risks

Higher security control over internal servers and networks

Easy to scale as the business need changes (Nodes, users, data storage)

Requires change to infrastructure and licenses

Updates and patches done by 3rd party vendor Must rely on internal resources that could be time consuming or expensive tech support


Start small

Build partnerships

Clarify the Business Value (ROI)

Collect data “ONLY” to solve a problem

Don’t try to solve a problem because you have data

Understand your culture and change management

Security first

Understand monitor vs. control

Involve your key stakeholders

the guys who does the real work

Get everyone's buy-in

Checklist for a successful IIoT Project Implementation


Bhanu Srilla, MS, CESCP, CMRP

Director of Product Marketing

[email protected]

Grace Engineered Products, Inc.

1-800-280-9517

Questions & Answers

39

mailto:[email protected]

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