Abstract

Condition monitoring plays a crucial role in improving system failure resilience, preventing tragic consequences brought by unexpected system failure events, and saving the consequential high operation and maintenance costs. Continuous condition monitoring systems have been applied to diversified engineering systems for well-informed operational decision-makings. Although research has been devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers to assess the value of such monitoring systems at the design stage therefore making system design decisions on adopting monitoring systems to maximize the benefits. This paper constructs such a decision-making framework based on the value of information, with which system designers can evaluate expected operation cost reductions under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies on a battery energy storage system and a mechanical system, respectively, are employed to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. Case study results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of costs involved in the operating process. The adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.

References

1.
Hazelrigg
,
G. A.
,
1998
, “
A Framework for Decision-Based Engineering Design
,”
ASME J. Mech. Des.
,
120
(
4
), pp.
653
658
.
2.
Hu
,
Z.
, and
Du
,
X.
,
2013
, “
Time-Dependent Reliability Analysis With Joint Upcrossing Rates
,”
Struct. Multidiscipl. Optim.
,
48
(
5
), pp.
893
907
.
3.
Li
,
J.
, and
Mourelatos
,
Z. P.
,
2009
, “
Time-Dependent Reliability Estimation for Dynamic Problems Using a Niching Genetic Algorithm
,”
ASME J. Mech. Des.
,
131
(
7
), p.
071009
.
4.
Wang
,
P.
,
Wang
,
Z.
, and
Almaktoom
,
A. T.
,
2014
, “
Dynamic Reliability-Based Robust Design Optimization With Time-Variant Probabilistic Constraints
,”
Eng. Optim.
,
46
(
6
), pp.
784
809
.
5.
Wang
,
Z.
, and
Wang
,
P.
,
2012
, “
A Nested Extreme Response Surface Approach for Time-Dependent Reliability-Based Design Optimization
,”
ASME J. Mech. Des.
,
134
(
12
), p.
121007
.
6.
Zhang
,
J.
, and
Du
,
X.
,
2011
, “
Time-Dependent Reliability Analysis for Function Generator Mechanisms
,”
ASME J. Mech. Des.
,
133
(
3
), p.
031005
.
7.
Wang
,
Z.
, and
Wang
,
P.
,
2013
, “
A New Approach for Reliability Analysis With Time-Variant Performance Characteristics
,”
Reliab. Eng. Syst. Saf.
,
115
(
1
), pp.
70
81
.
8.
Wang
,
Z.
,
Huang
,
H.-Z.
, and
Du
,
X.
,
2010
, “
Optimal Design Accounting for Reliability, Maintenance, and Warranty
,”
ASME J. Mech. Des.
,
132
(
1
), p.
011007
.
9.
Wang
,
P.
,
Wang
,
Z.
,
Youn
,
B. D.
, and
Lee
,
S.
,
2015
, “
Reliability-Based Robust Design of Smart Sensing Systems for Failure Diagnostics Using Piezoelectric Materials
,”
Comput. Struct.
,
156
(
1
), pp.
110
121
.
10.
Wang
,
P.
,
Youn
,
B. D.
,
Hu
,
C.
,
Ha
,
J. M.
, and
Jeon
,
B.
,
2015
, “
A Probabilistic Detectability-Based Sensor Network Design Method for System Health Monitoring and Prognostics
,”
J. Intell. Mater. Syst. Struct.
,
26
(
9
), pp.
1079
1090
.
11.
Wang
,
P.
,
Tamilselvan
,
P.
, and
Hu
,
C.
,
2014
, “
Health Diagnostics Using Multi-attribute Classification Fusion
,”
Eng. Appl. Artif. Intell.
,
32
(
1
), pp.
192
202
.
12.
Bai
,
G.
, and
Wang
,
P.
,
2016
, “
Prognostics Using an Adaptive Self-cognizant Dynamic System Approach
,”
IEEE Trans. Reliab.
,
65
(
3
), pp.
1427
1437
.
13.
Almaktoom
,
A. T.
,
Krishnan
,
K. K.
,
Wang
,
P.
, and
Alsobhi
,
S.
,
2016
, “
Cost Efficient Robust Global Supply Chain System Design Under Uncertainty
,”
Int. J. Adv. Manuf. Technol.
,
85
(
1
), pp.
853
868
.
14.
Yodo
,
N.
, and
Wang
,
P.
,
2016
, “
Engineering Resilience Quantification and System Design Implications: A Literature Survey
,”
ASME J. Mech. Des.
,
138
(
11
), p.
111408
.
15.
Ramani
,
K.
,
Ramanujan
,
D.
,
Bernstein
,
W. Z.
,
Zhao
,
F.
,
Sutherland
,
J.
,
Handwerker
,
C.
,
Choi
,
J.-K.
,
Kim
,
H.
, and
Thurston
,
D.
,
2010
, “
Integrated Sustainable Life Cycle Design: A Review
,”
ASME J. Mech. Des.
,
132
(
9
), p.
091004
.
16.
Ma
,
H.
,
Chu
,
X.
,
Lyu
,
G.
, and
Xue
,
D.
,
2017
, “
An Integrated Approach for Design Improvement Based on Analysis of Time-Dependent Product Usage Data
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111401
.
17.
Behdad
,
S.
, and
Thurston
,
D.
,
2012
, “
Disassembly and Reassembly Sequence Planning Tradeoffs Under Uncertainty for Product Maintenance
,”
ASME J. Mech. Des.
,
134
(
4
), p.
041011
.
18.
Chung
,
W.-H.
,
Okudan Kremer
,
G. E.
, and
Wysk
,
R. A.
,
2014
, “
A Modular Design Approach to Improve Product Life Cycle Performance Based on the Optimization of a Closed-Loop Supply Chain
,”
ASME J. Mech. Des.
,
136
(
2
), p.
021001
.
19.
Sun
,
B.
,
Zeng
,
S.
,
Kang
,
R.
, and
Pecht
,
M. G.
,
2012
, “
Benefits and Challenges of System Prognostics
,”
IEEE Trans. Reliab.
,
61
(
2
), pp.
323
335
.
20.
Liu
,
X.
,
Zheng
,
Z.
,
Toy
,
E. B.
,
Zhou
,
Z.
, and
Wang
,
P.
,
2021
, “
Battery Asset Management With Cycle Life Prognosis
,”
Reliab. Eng. Syst. Saf.
,
216
(
1
), p.
107948
.
21.
Liu
,
X.
, and
Wang
,
P.
,
2020
, “
Maintenance Decision Making Using State Dependent Markov Analysis With Failure Couplings
,”
Proceedings of the 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)
,
Vancouver, Canada
,
Aug. 20–23
, pp.
1
6
.
22.
Ferentinos
,
K. P.
, and
Tsiligiridis
,
T. A.
,
2007
, “
Adaptive Design Optimization of Wireless Sensor Networks Using Genetic Algorithms
,”
Computer Networks
,
51
(
4
), pp.
1031
1051
.
23.
Rathi
,
S.
, and
Gupta
,
R.
,
2014
, “
Sensor Placement Methods for Contamination Detection in Water Distribution Networks: A Review
,”
Procedia Eng.
,
89
(
1
), pp.
181
188
.
24.
Gorjian
,
N.
,
Ma
,
L.
,
Mittinty
,
M.
,
Yarlagadda
,
P.
, and
Sun
,
Y.
,
2010
, “
A Review on Degradation Models in Reliability Analysis
,”
Engineering Asset Lifecycle Management
,
Athens, Greece
,
Sept. 20–23
, Springer, pp.
369
384
.
25.
Gorjian
,
N.
,
Ma
,
L.
,
Mittinty
,
M.
,
Yarlagadda
,
P.
, and
Sun
,
Y.
,
2010
, “
A Review on Reliability Models With Covariates
,”
Engineering Asset Lifecycle Management
,
Springer
, pp.
385
397
.
26.
Bian
,
L.
, and
Gebraeel
,
N.
,
2014
, “
Stochastic Modeling and Real-Time Prognostics for Multi-Component Systems With Degradation Rate Interactions
,”
IIE Trans.
,
46
(
5
), pp.
470
482
.
27.
Guo
,
C.
,
Wang
,
W.
,
Guo
,
B.
, and
Si
,
X.
,
2013
, “
A Maintenance Optimization Model for Mission-Oriented Systems Based on Wiener Degradation
,”
Reliab. Eng. Syst. Saf.
,
111
(
1
), pp.
183
194
.
28.
Do
,
P.
,
Voisin
,
A.
,
Levrat
,
E.
, and
Iung
,
B.
,
2015
, “
A Proactive Condition-Based Maintenance Strategy With Both Perfect and Imperfect Maintenance Actions
,”
Reliab. Eng. Syst. Saf.
,
133
(
1
), pp.
22
32
.
29.
Alaswad
,
S.
, and
Xiang
,
Y.
,
2017
, “
A Review on Condition-Based Maintenance Optimization Models for Stochastically Deteriorating System
,”
Reliab. Eng. Syst. Saf.
,
157
(
1
), pp.
54
63
.
30.
Guillen
,
A. J.
,
Crespo
,
A.
,
Gomez
,
J. F.
, and
Sanz
,
M. D.
,
2016
, “
A Framework for Effective Management of Condition Based Maintenance Programs in the Context of Industrial Development of e-Maintenance Strategies
,”
Comput. Ind.
,
82
(
1
), pp.
170
185
.
31.
Huynh
,
K. T.
,
Barros
,
A.
, and
Berenguer
,
C.
,
2012
, “
Maintenance Decision-Making for Systems Operating Under Indirect Condition Monitoring: Value of Online Information and Impact of Measurement Uncertainty
,”
IEEE Trans. Reliab.
,
61
(
2
), pp.
410
425
.
32.
Fauriat
,
W.
, and
Zio
,
E.
,
2020
, “
Optimization of an Aperiodic Sequential Inspection and Condition-Based Maintenance Policy Driven by Value of Information
,”
Reliab. Eng. Syst. Saf.
,
204
(
1
), p.
107133
.
33.
Clemen
,
R. T.
, and
Reilly
,
T.
,
2013
,
Making Hard Decisions With Decision Tools
,
Cengage Learning
,
Boston, MA
.
34.
Parnell
,
G. S.
,
Terry Bresnick
,
M.
,
Tani
,
S. N.
, and
Johnson
,
E. R.
,
2013
,
Handbook of Decision Analysis
, Vol.
6
,
John Wiley & Sons
,
Hoboken, NJ
.
35.
Pozzi
,
M.
, and
Der Kiureghian
,
A.
,
2011
, “
Assessing the Value of Information for Long-Term Structural Health Monitoring
,”
Proceedings of the Health Monitoring of Structural and Biological Systems
,
San Francisco, CA
, p. 79842W.
36.
Ahmad
,
R.
, and
Kamaruddin
,
S.
,
2012
, “
An Overview of Time-Based and Condition-Based Maintenance in Industrial Application
,”
Comput. Ind. Eng.
,
63
(
1
), pp.
135
149
.
37.
Tian
,
Z.
,
Wu
,
B.
, and
Chen
,
M.
,
2014
, “
Condition-Based Maintenance Optimization Considering Improving Prediction Accuracy
,”
J. Oper. Res. Soc.
,
65
(
9
), pp.
1412
1422
.
38.
Gebraeel
,
N.
,
2006
, “
Sensory-updated Residual Life Distributions for Components With Exponential Degradation Patterns
,”
IEEE Trans. Autom. Sci. Eng.
,
3
(
4
), pp.
382
393
.
39.
Marble
,
S.
, and
Morton
,
B. P.
,
2006
, “
Predicting the Remaining Life of Propulsion System Bearings
,”
Proceedings of the 2006 IEEE Aerospace Conference
,
IEEE
, p.
8
.
40.
Huynh
,
K. T.
,
Grall
,
A.
, and
Berenguer
,
C.
,
2018
, “
A Parametric Predictive Maintenance Decision-Making Framework Considering Improved System Health Prognosis Precision
,”
IEEE Trans. Reliab.
,
68
(
1
), pp.
375
396
.
41.
Wang
,
J.
,
Liu
,
P.
,
Hicks-Garner
,
J.
,
Sherman
,
E.
,
Soukiazian
,
S.
,
Verbrugge
,
M.
,
Tataria
,
H.
,
Musser
,
J.
, and
Finamore
,
P.
,
2011
, “
Cycle-Life Model for Graphitelifepo4 Cells
,”
J. Power Sources
,
196
(
8
), pp.
3942
3948
.
You do not currently have access to this content.