International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.

[1]  M. Lee.  Production Machinery Maintenance Cost Optimization: A Review , 2016 .

[2]  Bo Guo,et al.  Real-time Reliability Evaluation with a General Wiener Process-based Degradation Model , 2014, Qual. Reliab. Eng. Int..

[3]  Tshilidzi Marwala,et al.  EARLY CLASSIFICATIONS OF BEARING FAULTS USING HIDDEN MARKOV MODELS, GAUSSIAN MIXTURE MODELS, MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND FRACTALS , 2006 .

[4]  Sriram Narasimhan,et al.  A hybrid hidden Markov model towards fault detection of rotating components , 2017 .

[5]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[6]  Hoon Sohn,et al.  VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL , 2001 .

[7]  Radislav Smid,et al.  Condition Indicators for Gearbox Condition Monitoring Systems , 2005 .

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Eric Bechhoefer,et al.  Techniques for Large, Slow Bearing Fault Detection , 2020 .

[10]  Ying Peng,et al.  A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods , 2012 .

[11]  G. Klutke,et al.  Reliability and Life-Cycle Analysis of Deteriorating Systems , 2015 .

[12]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[13]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[14]  Uday Kumar,et al.  Reliability analysis of hydraulic systems of LHD machines using the power law process model , 1992 .

[15]  Hamid Reza Karimi,et al.  Data-driven design of robust fault detection system for wind turbines , 2014 .

[16]  Kwok-Leung Tsui,et al.  Condition monitoring and remaining useful life prediction using degradation signals: revisited , 2013 .

[17]  Brigitte Chebel-Morello,et al.  Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network , 2015 .

[18]  Aditya Sharma,et al.  Feature extraction and fault severity classification in ball bearings , 2016 .

[19]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[20]  G A Whitmore,et al.  Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation , 1997, Lifetime data analysis.

[21]  Asok Ray,et al.  Symbolic time series analysis for anomaly detection: A comparative evaluation , 2005, Signal Process..

[22]  Robert Randall,et al.  Vibration–based Condition Monitoring , 2011 .

[23]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[24]  Carey Bunks,et al.  CONDITION-BASED MAINTENANCE OF MACHINES USING HIDDEN MARKOV MODELS , 2000 .

[25]  Ming Liang,et al.  Detection and diagnosis of bearing and cutting tool faults using hidden Markov models , 2011 .

[26]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[27]  Georgia-Ann Klutke,et al.  Degradation: Data Analysis and Analytical Modeling , 2016 .

[28]  Wenbin Wang,et al.  Early defect identification: application of statistical process control methods , 2008 .

[29]  Bruce R. Ellingwood,et al.  Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants , 1993 .

[30]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[31]  H. Akaike.  A new look at the statistical model identification , 1974 .

[32]  Nagi Gebraeel,et al.  Predictive Maintenance Management Using Sensor-Based Degradation Models , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[33]  Michael G. Lipsett,et al.  Fault detection using transient machine signals , 2008 .

[34]  Uwe D. Hanebeck,et al.  Data association in a world model for autonomous systems , 2010, 2010 IEEE Conference on Multisensor Fusion and Integration.

[35]  Viliam Makis,et al.  Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis , 2007 .