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Red Track, Session 3
2018 ARDC India
Condition Monitoring in Process Industry
‐ Case Study
Prof. Sathyanarayana A, FIMechE
Begins at: 11:35 AM, Wednesday, August 8th
PRESENTATION SLIDES
The following presentation was delivered at the:
Applied Reliability and Durability Conference, India
August 8 - 10, 2018: Chennai, India
http://www.ardconference.com
The Applied Reliability and Durability Conference (ARDC) is intended to be a forum for reliability, maintainability and
durability practitioners within industry and government to discuss their success stories and lessons learned regarding
the application of reliability and durability techniques to meet real world challenges. Each year, the ARDC issues an
open "Call for Presentations" at http://www.ardconference.com and the presentations delivered at the Conference are
selected on the basis of the presentation proposals received.
Although the ARDC may edit the presentation materials as needed to make them ready to print, the content of the
presentation is solely the responsibility of the author. Publication of these presentation materials in the ARDC
Proceedings does not imply that the information and methods described in the presentation have been verified or
endorsed by the ARDC and/or its organizers.
The publication of these materials in the ARDC presentation format is
Copyright © 2018 International Applied Reliability Symposium LLC, All Rights Reserved.
Sathyanarayana Appia, Air Liquide Slide Number: 2Session 3Red Track
• Introduction to Condition Monitoring in Process Industry
• Fault Detection, Diagnosis and Prognosis 
• Fault Management
• Condition Monitoring System
• Fault Detection – Process Model
• Fault Diagnosis – Plausibility Checks
• Data Analysis 
• Case Study
• On‐line Enterprise Asset Management
• Remote Monitoring and Maintenance – IOT at Air Liquide
• Acknowledgment and References
• Questions 
Overview
Sathyanarayana Appia, Air Liquide Slide Number: 3Session 3Red Track
Run to Break - Unplanned Maintenance
 Maintenance as Cost Centre
 Maintenance cost greater than 20% of overall 
cost
Preventive Maintenance – Planned Maintenance
 Too Much Maintenance
Predictive Maintenance - “Condition Based
Maintenance”
 Profit Centre
Condition Monitoring in Industry
Sathyanarayana Appia, Air Liquide Slide Number: 4Session 3Red Track
Vibration Analysis
Lubricant Analysis
Performance Analysis
Thermography
Condition Based Maintenance
Sathyanarayana Appia, Air Liquide Slide Number: 5Session 3Red Track
o Fault is a deviation / abnormal condition – a fault may 
initiate failure.(State)
o Failure is a permanent interruption of system (Event)
o Malfunction is temporary interruption
 Fault Detection: Early detection of  faults (small faults) with 
abrupt / incipient time behaviour; e.g., Limit Checking / 
Threshold checking, Trend checking, Signal analysis….
 Fault Diagnosis : Diagnosis of faults in the processes / parts/ 
devices; e.g., Analytical / Heuristic symptoms and their 
relations to faults, Fault evaluation (Hazard class)
 Fault Prognosis: Predicting remaining useful life
 E‐ MAINTENANCE
Fault Detection, Diagnosis & Prognosis
Sathyanarayana Appia, Air Liquide Slide Number: 6Session 3Red Track
 Avoid shutdowns by early detection and actions like 
condition based maintenance / repair
 Fault tolerant systems for sudden faults / failures / 
malfunctions – Reconfiguration / Redundant 
components
 Maintenance on demand
 Tele‐monitoring
 100% Quality Control
 Effective asset management
 Improving total life cycles of products and processes
Fault Management
Sathyanarayana Appia, Air Liquide Slide Number: 7Session 3Red Track
Condition Monitoring System
Process
Monitoring
System
RTU
Ethernet
Cloud Network
Monitoring
System
Monitoring
System
Machine
With Vibration
Sensors
Process
Sathyanarayana Appia, Air Liquide Slide Number: 8Session 3Red Track
Fault Detection – Process Model
RTU
Real Time Data
Transfer
OLE
Monitoring
System
Microsoft Excel /
VB/ Python
P
P
P P
P
If ΔP High –> Change Suction Filter
Atm.
Pressure
Compressor
Suction
Pressure
Sathyanarayana Appia, Air Liquide Slide Number: 9Session 3Red Track
 First step towards Model Based Fault Detection
‐ By checking the plausibility of its indicated values (Rough 
Process Model)
‐ Using Microsoft Excel / VB / Python
‐ For above example:
 IF [ΔP > ΔPmax] THEN [CHANGE SUCTION FILTER]
‐ More examples: Oil Pressure of engine Poil with speed N and 
C.W Tw
‐ IF[N < 2000 rpm] AND [Tw < 40 C] THEN [2.5 bar < Poil > 4 bar}
‐ Trend Analysis using “LOOKUP” function in Excel
‐ Use VBA macros to “Diagnose Faults” 
Fault Diagnosis – Plausibility Checks
Sathyanarayana Appia, Air Liquide Slide Number: 10Session 3Red Track
Process Model
Mass of Air in, mai
Mass of water in, mwi
Temperature
Tai
Two
Twi
Tao
ma
mw
Tai
Ta0
Twi
Two
Overall Fault Detection of H.E – Static Model
Heat loss, Ql = ma cpa (Tai-Tao) – mw cpw (Two-Twi)
Overall Heat transfer coeff, U = Q/(AΔTm)
ΔTm = (Tai-Two)-(Tao-Twi)/ ln (Tai-Two)/(Tao-Twi)
Residual , r(k) = Ql(k)/cp, k = t/To, r(k) will change in
case of faults in sensors, leaks, insulation …
Evaluating U(operation) with
U(design ) can determine
contamination / fouling in H.E.
Sathyanarayana Appia, Air Liquide Slide Number: 11Session 3Red Track
On‐board Diagnostics (OBD)
Sathyanarayana Appia, Air Liquide Slide Number: 12Session 3Red Track
Data Analysis
Name Cell Sim# Graph Min  Mean  Max  5% 95%Errors
Range: RISK (Loss Hours)
New Reliable Plant with Backup / 
RISK (Loss Hours)
F5 1 13.04665 46.77504 113.4808 20.81343 79.74958 0
New Reliable Plant with Backup / 
RISK (Loss Hours)
F5 2 13.04665 46.77504 113.4808 20.81343 79.74958 0
New Reliable Plant with Backup / 
RISK (Loss Hours)
F5 3 13.04665 46.77504 113.4808 20.81343 79.74958 0
New Reliable Plant with Backup / 
RISK (Loss Hours)
F5 4 13.04665 46.77504 113.4808 20.81343 79.74958 0
Low customer demand / RISK (Loss 
Hours)
F6 1 32.93708 125.7655 312.3814 53.29559 217.4649 0
Low customer demand / RISK (Loss 
Hours)
F6 2 32.93708 125.7655 312.3814 53.29559 217.4649 0
Inputs in 
Scenario For  
F5 >75%
Cell Name Description Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#1)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#1)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#1)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#2)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#2)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#2)
Percentile
Risk 
Assessment!F5
New Reliable Plant 
with Backup / RISK 
(Loss Hours)
(Sim#3)
Percentile
>75% <25% >90% >75% <25% >90% >75%
#1 D5
New Reliable Plant with Backup / 
Probability of occurance(Failure)
RiskWeibull(2,0.327,RiskShift(0.1),Ri
skStatic(0.1))
0.874 0.126 0.95 0.874 0.126 0.95 0.874
‐ D8
Unreliable Backup/ 
Maintenance/Equipment Service / 
Probability of occurance(Failure)
RiskWeibull(5,0.5,RiskShift(0.3),Risk
Static(0.6))
‐ ‐ ‐ ‐ ‐ ‐ ‐
‐ D7
Unreliable Maintenance / 
Equipment Service / Probability of 
occurance(Failure)
RiskWeibull(5,0.696,RiskShift(0.1),Ri
skStatic(0.6))
‐ ‐ ‐ ‐ ‐ ‐ ‐
‐ D6
Low customer demand / Probability 
of occurance(Failure)
RiskWeibull(2,0.38,RiskShift(0.1),Ris
kStatic(0.5))
‐ ‐ ‐ ‐ ‐ ‐ ‐
Sathyanarayana Appia, Air Liquide Slide Number: 13Session 3Red Track
Case Study – Nitrogen Generator
Air
Compressor Carbon Filter
Heater
Membrane A
Membrane B
Nitrogen
Cryogenic
Technology
Pressure swing adsorption
(PSA)
Membrane
Technology
Sathyanarayana Appia, Air Liquide Slide Number: 14Session 3Red Track
Modelling of On‐site Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 15Session 3Red Track
Suction Air‐filter:
Microsoft excel plausibility checks, of ΔP with 
respect to ΔPmax  is shown below:
CASE: Model-based diagnosis of Cryogenic
Nitrogen Generator
	 ∆ ∆ , "CLEAN	AIR	FILTER"
Sathyanarayana Appia, Air Liquide Slide Number: 16Session 3Red Track
Inter‐coolers
Static Model of Heat Exchanger
Heat Loss,  	
∆
ln
Overall Heat Transfer Coefficient (Operational & Design)
	∆
,
1
1 1
CASE: Model‐based diagnosis of Cryogenic Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 17Session 3Red Track
Pipe leaks and Pipe clogging
CASE: Model‐based diagnosis of Cryogenic Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 18Session 3Red Track
Overall Reliability Model of Cryogenic Nitrogen 
Generator with Back‐up Liquid Storage
CASE: Model‐based diagnosis of Cryogenic Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 19Session 3Red Track
CASE: Model‐based diagnosis of Cryogenic Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 20Session 3Red Track
Reliability, Availability and Maintainability study 
of several subsystems:
CASE: Model‐based diagnosis of Cryogenic Nitrogen Generator
Sathyanarayana Appia, Air Liquide Slide Number: 21Session 3Red Track
Failure and Defect Analysis
Sathyanarayana Appia, Air Liquide Slide Number: 22Session 3Red Track
Failure Rate Model
Sathyanarayana Appia, Air Liquide Slide Number: 23Session 3Red Track
Typical Failure Rate Curve
Sathyanarayana Appia, Air Liquide Slide Number: 24Session 3Red Track
Reliability Block Diagram
Sathyanarayana Appia, Air Liquide Slide Number: 25Session 3Red Track
RBD – Sub Systems
Sathyanarayana Appia, Air Liquide Slide Number: 26Session 3Red Track
Reliability, Availability and Maintainability 
of On‐Site Generator
Sathyanarayana Appia, Air Liquide Slide Number: 27Session 3Red Track
RAM Study – Modified System
Sathyanarayana Appia, Air Liquide Slide Number: 28Session 3Red Track
RAM Study – Modified System
Sathyanarayana Appia, Air Liquide Slide Number: 29Session 3Red Track
On‐Site Nitrogen Generator –
Scenarios
Sathyanarayana Appia, Air Liquide Slide Number: 30Session 3Red Track
On‐line Enterprise Asset Management
• Uptime / Downtime – Plant Availability
• Total Production – Revenue Generation
• Total cost of Maintenance …
• ROCE – Return on Capital Employed
RTU
Sathyanarayana Appia, Air Liquide Slide Number: 31Session 3Red Track
IoT
 Internet of Things Applications at AIR LIQUIDE 
 Remote Monitoring and Maintenance
Sathyanarayana Appia, Air Liquide Slide Number: 32Session 3Red Track
Air Liquide Group
 World leader in gases, technologies and services for
Industry and Health, Air Liquide is present in 80
countries with more than 50,000 employees and serves
more than 2 million customers and patients.
 Oxygen, nitrogen and hydrogen have been at the core of
the company’s activities since its creation in 1902.
 Air Liquide’s revenues amounted to € 15.4 billion in
2014, and its solutions that protect life and the
environment represented more than 40% of sales. Air
Liquide is listed on the Paris Euronext stock exchange
(compartment A) and is a member of the CAC 40 and
Dow Jones Euro Stoxx 50 indexes.
Source: https://www.airliquide.com/
Sathyanarayana Appia, Air Liquide Slide Number: 33Session 3Red Track
Air Liquide in India
 The Engineering entity started in 1992
 The Gases entity started in 1996
 Head office at New Delhi
 More than 1000 employees
 Global E&C Solutions based in New Delhi
Source: http://www.in.airliquide.com/#
Sathyanarayana Appia, Air Liquide Slide Number: 34Session 3Red Track
IoT History at Air Liquide
 1990: Pilot Projects 
 1997‐2004:  1st Phase  deployment  (case to case) 
 2005‐ : Web based technologies and deployment of M2M
Source: Télémaintenance & Télégestion : Retour d’expériences chez Air Liquide après 10
an, http://www.clubautomation.org/
Sathyanarayana Appia, Air Liquide Slide Number: 35Session 3Red Track
IoT Applications
 Remote monitoring of unmanned process plants using 
TELEFLO
 Remote monitoring and Data acquisition of several on‐site 
gas generators
 Bulk Liquid Storage Tank – Level monitoring using 
Telemetry
 DATAL: Wireless Gas Management system
 Condition Monitoring of machineries
 Plant Maintenance Management  & Management of 
Alerts
 Intelligent system
Sathyanarayana Appia, Air Liquide Slide Number: 36Session 3Red Track
Remote monitoring of process plants
Process Plant
PLC
TELEFLO
Pager
TELEFLO SERVER
Remote PC
Mobile Phone/
SMS
Customer
Sathyanarayana Appia, Air Liquide Slide Number: 37Session 3Red Track
Remote monitoring of On‐Sites
Onsite Plants
TELEFLO
Pager
TELEFLO SERVER
Mobile Phone/
SMS
Remote PC
Customers
Sathyanarayana Appia, Air Liquide Slide Number: 38Session 3Red Track
Bulk Liquid Storage Tank Monitoring
• Gas consumption
monitoring
• Invoicing
TELEFLO SERVER
LIQUID SUPPLY
NETWORK
Customer
Sathyanarayana Appia, Air Liquide Slide Number: 39Session 3Red Track
DATAL – Wireless gas management 
system
Sathyanarayana Appia, Air Liquide Slide Number: 40Session 3Red Track
Condition Monitoring of machineries
Process PlantPLCTELEFLO
Remote PC
TELEFLO SERVER
CONTROL ROOM
OLE - EXCEL
• Real-time analysis of process
equipment using Object Linking and
Embedding (OLE) in MS Excel
• Using the power of EXCEL in plotting
graphs and setting alerts for pro-active
maintenance
Sathyanarayana Appia, Air Liquide Slide Number: 41Session 3Red Track
Plant Maintenance Management  & 
Management of Alerts*
Remote PC
 Plant Maintenance software was
developed in-house (India) using Visual
Basic
 It incorporated predictive and planned
maintenance in the system
 Maintenance Records and Reports
*Management of Alerts - Conceptualized
Sathyanarayana Appia, Air Liquide Slide Number: 42Session 3Red Track
Intelligent system
 Conceptualized in India (1998‐2000)
 An Excel based application which can 
trouble shoot intelligently  using simple 
logic operators in Excel and Flow charts.
 It was in development stage, never 
implemented
Sathyanarayana Appia, Air Liquide Slide Number: 43Session 3Red Track
IoT at Air Liquide Today
Sathyanarayana Appia, Air Liquide Slide Number: 44Session 3Red Track
Source: Quelles technologies et méthodes pour les automatismes de demain?
https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwi2oPni6eDJAhXGt44KHXVa
DaQQFgghMAE&url=http%3A%2F%2Fwww.histoire-du-club-
automation.org%2Fjournees2006%2FsessionBdecembre2006.pdf&usg=AFQjCNHC4EONOzLaq2SKGLJerX1SZnb44A&bvm=bv.110
151844,d.c2E
Sathyanarayana Appia, Air Liquide Slide Number: 45Session 3Red Track
Reliability with IoT
 TELEFLO can find MTBF for each equipment and 
part connected with sensors using a simple Pascal 
code. It can also determine λ (Failure rate) and 
record it in the database.
 Availability of the unit can be calculated and 
recorded in TELEFLO using a Pascal code.
 It is also possible to send data to OEM for spare 
parts management  
 Using TELEFLO – Telemetry system: Availability of 
the unit can be improved. 
Sathyanarayana Appia, Air Liquide Slide Number: 46Session 3Red Track
Risk Assessment of IoT based on experience
 Read only data is safe but in the case of read and write data 
register there is associated risk.
 IoT is safe to read data based on Hierarchy of  Data
 Is Modbus secure?
 Flashing of ROM in Teleflo results in reset of PLC ‐High Risk.
 Operating On/Off remotely not safe at all – High Risk.
 Equipment on maintenance /repair or under maintenance  ‐
Remote access or operation can result in damage to equipment 
or injury to persons. 
 Fail‐safe operation should be automatic and local. Not through 
Remote monitoring and control.
 Physical system is ‘complex’ and are not ‘binary’.
Sathyanarayana Appia, Air Liquide Slide Number: 47Session 3Red Track
Industrie 4.0
Sathyanarayana Appia, Air Liquide Slide Number: 48Session 3Red Track
Thanks to Microsystems Team
Sathyanarayana Appia, Air Liquide Slide Number: 49Session 3Red Track
• Air Liquide , France
References
 Fault Diagnosis Systems by Rolf Isermann, Springer 2006
 Fault Diagnosis Applications by Rolf Isermann, Springer
 Vibration based Condition Monitoring by Robert Bond Randall, 
WILEY 2011
 Artificial Intelligence Tools: Decision support Systems in 
Condition Monitoring and Diagnosis by Diego Galar Pascual, 
CRC Press 2015
 https://opcfoundation.org/
 Military Handbook and Standards Related to Reliability
 http://www.weibull.com/
 http://reliawiki.com/index.php/Basics_of_System_Reliability_A
nalysis
Acknowledgment
Sathyanarayana Appia, Air Liquide Slide Number: 50Session 3Red Track
Questions
Thank you for your attention.
Do you have any questions?
Sathyanarayana Appia, Air Liquide Slide Number: 51Session 3Red Track
Sathyanarayana Appia
Prof. Sathyanarayana Appia has over 28 years of 
experience in industry and academics. He has extensively 
worked hands‐on in the areas of Reliability, Risk 
Assessment, Remote monitoring and Data Analysis of 
On‐site Projects at Air Liquide.  He holds Bachelor of 
Engineering in Mechanical Engineering from V.N.I.T, 
Nagpur and Masters of Technology from I.I.T, Kharagpur. 
He has been recently elected as the Fellow of Institution 
of Mechanical Engineers, U.K. He has travelled and 
worked in Europe and India and has interests in learning 
new languages and cultures.
Sathyanarayana Appia, Air Liquide Slide Number: 52Session 3Red Track
Thank You
ಧನಯ್ವಾದ
ध यवाद
ありがとうございました
谢谢
Danke
Merci

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