Data Driven Methods for Analysis and Design of Industrial Alarm Systems

117 Pages·2017·2.21 MB·English

Data Driven Methods for Analysis and Design of
Industrial Alarm Systems
by
Muhammad Shahzad Afzal
A thesis submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in
Control Systems
Department of Electrical and Computer Engineering
University of Alberta
(cid:2)c Muhammad Shahzad Afzal 2017
Abstract
An alarm system is an integral part of process monitoring and safety. A
poorly configured alarm system can sometimes cause more harm than good,
by introducing many false and nuisance alarms for the operators. Various
standards on alarm system management and rationalization suggest many
configuration methods to help in improving the overall performance of alarm
monitoring systems. In this thesis, the problem of analyzing and designing
alarm systems for both single- and multi-mode processes is considered.
A design procedure of a multivariate alarm system for multi-mode pro-
cesses is developed. A hidden Markov model based modeling approach is
adopted to capture the multi-modality of data and the mode-reachability con-
straints of a multi-mode process. A monitoring index utilizing the proposed
two-step Viterbi algorithm is developed, and for fault isolation, reconstruction
based contribution plots are used.
The utility of delay-timers in improving existing univariate alarm systems
formulti-modeprocessesisstudied. Amathematicalmodelisdevelopedtocal-
culate analytical expressions for different performance indices (the false alarm
rate, missed alarm rate, and expected detection delay). A particle swarm
optimization based method is proposed for designing delay-timers, while sat-
isfying the constraints on the performance indices and delay-timer lengths for
various modes of the operation of a process.
The analysis and design of time-deadbands for univariate alarm systems
ii
is also considered in this thesis. In particular, a Markov chain process based
mathematicalmodelisdevelopedtocapturethetime-deadbandconfigurations
for single mode processes. Analytical expressions for the performance indices
are calculated, and design procedures based on process data and alarm data
are developed.
Keywords: Alarm systems, Hidden Markov models, Markov processes, Delay-
timers, Time-deadbands
iii
Preface
The research work conducted in Chapter 2 was part of the collaboration
with Dr.Wen Tan, who was with the University of Alberta as a Research
Associate at the time of this work. The ideas presented in Chapter 3 were
my original ideas. The work presented in Chapter 4 was a result of collabo-
ration with Dr.Iman Izadi and Ali Bandehkhoda from Isfahan University of
Technology, Iran.
• Chapter 2 has been published as: Afzal, M.S., Tan, W., & Chen, T.,
“Process Monitoring for Multimodal Processes with Mode-Reachability
Constraints”, IEEE Transactions on Industrial Electronics, vol. 64, pp.
4325-4335, 2017.
• Chapter 3 has been published as: Afzal, M.S., & Chen, T., “Analy-
sis and Design of Multimode Delay-Timers”, Chemical Engineering and
Research Design, vol. 120, pp. 179-193, 2017.
• Chapter 4 has been submitted for publication as: Afzal, M.S., Chen, T.,
Bandehkhoda, A., & Izadi, I., “Analysis and Design of Time-deadbands
for Univariate Alarm Systems”, Control Engineering Practices. A pre-
liminary version of this chapter was presented in American Control Con-
ference (ACC) in Seattle, WA, USA May 24-26, 2017
iv
Acknowledgements
This thesis would not have been possible without the help and support of
the people around me, to only some of whom it is possible to give a special
mention here. Above all, I would like to pay my utmost gratitude to my
supervisor Prof.Tongwen Chen for his constant guidance and support. The
discussions we had during our individual and group meetings helped me a lot
in carrying out my research work and in writing this thesis. I could not have
imagined having a better mentor than him for my Ph.D. program.
I would also like to thank Dr.Wen Tan (North China Electric Power Uni-
versity), and Dr.Iman Izadi (Isfahan University of Technology, Iran) for their
suggestions in improving the work, and contributing towards my research.
My sincere thanks to Dr.Sirih L. Shah for sharing his ideas during different
workshops and meetings, and for his insightful comments on my work.
Last, but not the least, I would like to thank all of my former and cur-
rent research group members, with a special mention of Wenkai Hu, Ahmad
Al-Dabbagh, David Li, Ying Xiong, Ishtiza Azad, and Shiqi Lai for their en-
couragement, stimulating discussions, and for all the fun we had during the
last three years or so.
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Contents
1 Motivation and Background 1
1.1 Industrial process control . . . . . . . . . . . . . . . . . . . . . 1
1.2 Alarm systems . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Monitoring of Multi-mode Processes with Mode-reachability
Constraints 9
2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Hidden Markov models . . . . . . . . . . . . . . . . . . 13
2.2.2 Mode-reachability constraints . . . . . . . . . . . . . . 14
2.3 Proposed methodology . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 Offline training of an HMM model . . . . . . . . . . . 15
2.3.2 Online process monitoring . . . . . . . . . . . . . . . . 16
2.4 Application examples . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.1 A numerical example . . . . . . . . . . . . . . . . . . . 24
2.4.2 A continuous stirred tank heater . . . . . . . . . . . . 26
2.4.3 Performance comparison . . . . . . . . . . . . . . . . . 33
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Analysis and Design of Multi-mode Delay-Timers 36
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
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3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2.1 Types of alarm systems for multi-mode processes . . . 37
3.2.2 Long term behavior of an HMM model . . . . . . . . . 38
3.3 Modeling of multi-mode delay-timers . . . . . . . . . . . . . . 39
3.4 Performance assessment . . . . . . . . . . . . . . . . . . . . . 46
3.4.1 False alarm rate . . . . . . . . . . . . . . . . . . . . . . 47
3.4.2 Missed alarm rate . . . . . . . . . . . . . . . . . . . . . 47
3.4.3 Expected detection delay . . . . . . . . . . . . . . . . . 48
3.4.4 Simulation verification . . . . . . . . . . . . . . . . . . 52
3.5 Design of multi-mode delay-timers . . . . . . . . . . . . . . . . 53
3.5.1 Problem formulation . . . . . . . . . . . . . . . . . . . 54
3.5.2 Particle swarm optimization . . . . . . . . . . . . . . . 56
3.5.3 Design based on particle swarm optimization . . . . . . 57
3.5.4 Design examples . . . . . . . . . . . . . . . . . . . . . 59
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 Time-deadbands for Univariate Alarm Systems. 65
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.1 Types of deadbands . . . . . . . . . . . . . . . . . . . . 66
4.2.2 Run-length encoding of alarm sequences . . . . . . . . 68
4.3 Modeling of time-deadbands . . . . . . . . . . . . . . . . . . . 70
4.3.1 Long-term behavior of the model . . . . . . . . . . . . 73
4.3.2 Discussion on the assumptions . . . . . . . . . . . . . . 76
4.4 Performance assessment . . . . . . . . . . . . . . . . . . . . . 77
4.4.1 False alarm rate . . . . . . . . . . . . . . . . . . . . . . 77
4.4.2 Missed alarm rate . . . . . . . . . . . . . . . . . . . . . 78
4.4.3 Expected detection delay . . . . . . . . . . . . . . . . . 78
4.4.4 Simulation verification . . . . . . . . . . . . . . . . . . 80
4.5 Design of time-deadbands . . . . . . . . . . . . . . . . . . . . 80
4.5.1 Design based on process data . . . . . . . . . . . . . . 83
vii
4.5.2 Design based on alarm data . . . . . . . . . . . . . . . 84
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5 Conclusions and Future Work 93
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Bibliography 96
viii
List of Tables
1.1 Alarm system performance survey . . . . . . . . . . . . . . . . 4
2.1 Set points for modes of the CSTH [69] . . . . . . . . . . . . . 30
2.2 Missed and false alarm rates . . . . . . . . . . . . . . . . . . . 34
3.1 A performance comparison of design examples . . . . . . . . . 63
4.1 Recommendations for time-deadbands . . . . . . . . . . . . . 69
4.2 Performance comparison of the time-deadband configurations . 85
4.3 Performance comparison of time-deadband configuration . . . 91
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List of Figures
1.1 Industrial automation pyramid (ANSI/ISA-95) [20] . . . . . . 2
1.2 Alarm signals generation process . . . . . . . . . . . . . . . . 3
1.3 Taxonomy of alarm configuration methods . . . . . . . . . . . 5
2.1 Mode-reachability map . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Online process monitoring . . . . . . . . . . . . . . . . . . . . 17
2.3 Comparison of Viterbi algorithms . . . . . . . . . . . . . . . . 20
2.4 Scatter plot of data and Gaussian contours . . . . . . . . . . . 25
2.5 Mode detection during faulty scenario 1 (Example 1) . . . . . 26
2.6 Monitoring results for fault 1 for Example 1 . . . . . . . . . . 27
2.7 Monitoring results for fault 2 for Example 1 . . . . . . . . . . 28
2.8 Fault isolation results for Example 1 . . . . . . . . . . . . . . 29
2.9 Continuous stirred tank heater [56] . . . . . . . . . . . . . . . 29
2.10 Mode detection during faulty scenario 1 (Example 2) . . . . . 31
2.11 Monitoring results for fault 1 for CSTH . . . . . . . . . . . . . 32
2.12 Monitoring results for fault 2 for CSTH . . . . . . . . . . . . . 33
2.13 Fault isolation results for Example 2 . . . . . . . . . . . . . . 34
3.1 Types of alarm systems for multi-mode processes . . . . . . . 38
3.2 Two state HMM with discrete observations . . . . . . . . . . . 39
3.3 Single mode process with on delay-timer (n = 3), and off delay-
timer (m = 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Two-mode process (Mode 1: n = m = 2, and Mode 2: n = 3
1 1 2
m = 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2
3.5 HMMmodeloftheQ-modeprocesswithmulti-modedelay-timers 46
x