Fault Detection By Segment Evaluation Based On Inferential Statistics For Asset Monitoring
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Detection of unexpected events (e.g. anomalies and faults) from monitoring data is very challenging in machine health assessment. Hence, abrupt or incipient fault detection from the monitoring data is very crucial to increase asset safety, availability and reliability. This paper presents a generic methodology for abrupt and incipient fault detection and feature fusion for health assessment of complex systems. Proposed methodology consists of feature extraction, feature fusion, segmentation and fault detection steps. First of all, different features are extracted using descriptive statistics. Secondly, based on linearly weighted data fusion algorithm, extracted features are combined to get the generic and representative feature. Afterward, combined feature is divided into homogeneous segments by sliding window segmentation algorithm. Finally, each segment is further evaluated by coefficient of variability which is used in inferential statistics, to evaluate health state changes that indicate asset faults. To illustrate its effectiveness, the methodology is implemented on point machine and Li-ion battery monitoring data to detect abrupt and incipient faults. The results show that proposed methodology can be effectively used in fault detection for asset monitoring.
How to Cite
##plugins.themes.bootstrap3.article.details##
fault detection, feature extraction, Point machine monitoring, Time series segmentation, Segment evaluation, Li-ion Battery health assessment, feature fusion
Ardakani, Hossein Davari et al. 2012. “PHM for Railway System - A Case Study on the Health Assessment of the Point Machines.” PHM 2012 - 2012 IEEE Int. Conf.on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, Conference Program: 1–5.
Atamuradov, Vepa, and Fatih Camci. 2016. “Evaluation of Features with Changing Effectiveness for Prognostics.” Annual Conference of the Prognostics and Health Management Society 2016.
Bąkowski, Andrzej, Leszek Radziszewski, and Milan Žmindak. 2017. “Analysis of the Coefficient of Variation for Injection Pressure in a Compression Ignition Engine.” Procedia Engineering 177: 297–302. http://linkinghub.elsevier.com/retrieve/pii/S187770581730735X.
Camci, F., O. F. Eker, S. Baskan, and S. Konur. 2016. “Comparison of Sensors and Methodologies for Effective Prognostics on Railway Turnout Systems.” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 230(1): 24–42. http://pif.sagepub.com/lookup/doi/10.1177/0954409714525145.
Camci, F, C Ozkurt, O Toker, and V Atamuradov. 2015. “Sampling Based State of Health Estimation Methodology for Li-Ion Batteries.” Journal of Power Sources 278: 668–74.
D’Angelo, Marcos F S V et al. 2011. “Incipient Fault Detection in Induction Machine Stator-Winding Using a Fuzzy-Bayesian Change Point Detection Approach.” Applied Soft Computing Journal 11(1): 179–92.
Gebraeel, Nagi, Alaa Elwany, and Jing Pan. 2009. “Residual Life Predictions in the Absence of Prior Degradation Knowledge.” IEEE Transactions on Reliability 58(1): 106–17.
ISO 13372:2012. “Condition Monitoring and Diagnostics of Machines-Vocabulary.” http://viewer.afnor.org/Pdf/Viewer/?token=VR4VvnprAPA1.
Jardine, A. K S, Daming Lin, and Dragan Banjevic. 2006. “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance.” Mechanical Systems and Signal Processing 20(7): 1483–1510.
Keogh, E, S Chu, D Hart, and M Pazzani. 2003. “Segmenting Time Series: A Survey and Novel Approach.” Data Mining in Time Series Databases: 1–21.
Liu, Junqiang, Malan Zhang, Hongfu Zuo, and Jiwei Xie. 2014. “Remaining Useful Life Prognostics for Aeroengine Based on Superstatistics and Information Fusion.” Chinese Journal of Aeronautics 27(5): 1086–96. http://dx.doi.org/10.1016/j.cja.2014.08.013.
Pennacchi, Paolo et al. 2013. “Experimental Evidences in Bearing Diagnostics for Traction System of High Speed Trains.” Chemical Engineering Transactions 33: 739–44.
Bin Shams, M. A., H. M. Budman, and T. A. Duever. 2011. “Fault Detection, Identification and Diagnosis Using CUSUM Based PCA.” Chemical Engineering Science 66(20): 4488–98. http://dx.doi.org/10.1016/j.ces.2011.05.028.
Taylor, Wayne A. 2006. “Change-Point Analysis: A Powerful New Tool For Detecting Changes.” Analysis: 1–19.
Verbesselt, Jan, Rob Hyndman, Glenn Newnham, and Darius Culvenor. 2010. “Detecting Trend and Seasonal Changes in Satellite Image Time Series.” Remote Sensing of Environment 114(1): 106–15. http://dx.doi.org/10.1016/j.rse.2009.08.014.
Wang, Dong, Qiang Miao, and Michael Pecht. 2013. “Prognostics of Lithium-Ion Batteries Based on Relevance Vectors and a Conditional Three-Parameter Capacity Degradation Model.” Journal of Power Sources 239: 253–64. http://linkinghub.elsevier.com/retrieve/pii/S0378775313005235.
Williard, N;, W; He, M; Osterman, and M Pecht. 2013. “Comparative Analysis of Features for Determining State of Health in Lithium-Ion Batteries.” Int. J. Progn. Health Manag 2013(4): 1–7.
Wu, Yunkai, Bin Jiang, Ningyun Lu, and Donghua Zhou. 2015. “ToMFIR-Based Incipient Fault Detection and Estimation for High-Speed Rail Vehicle Suspension System.” Journal of the Franklin Institute 352(4): 1672–92. http://www.sciencedirect.com/science/article/pii/S0016003215000599.
Zhang, Bing, Andy C C Tan, and Jian hui Lin. 2016. “Gearbox Fault Diagnosis of High-Speed Railway Train.” Engineering Failure Analysis 66: 407–20. http://dx.doi.org/10.1016/j.engfailanal.2016.04.020.
Zhao, Qing, and Michel Kinnaert. 2009. “Statistical Properties of CUSUM Based Fault Detection Schemes for Fault Tolerant Control.” Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference: 7831–36. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5400825.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.