A Condition Based Maintenance Implementation for an Automated People Mover Gearbox

Ali Ashasi-Sorkhabi, Stanley Fong, Guru Prakash, and Sriram Narasimhan
Publication Target: 
IJPHM
Publication Issue: 
Special Issue on Railways & Mass Transportation
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Natural Sciences Engineering Research Council of Canada
AttachmentSizeTimestamp
ijphm_17_019.pdf2.22 MBSeptember 29, 2017 - 4:15am

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, non-stationary 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.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
019
Page Count: 
14
Submission Keywords: 
Condition Based Maintenance
APM transit
bearing diagnostics
Bayesian inference
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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