A Blind Condition-Based Maintenance Framework for Real-Time Fault Detection and Degradation Modeling of the LINK APM Gearbox

2020 ◽  
Author(s):  
Stanley Fong ◽  
Sriram Narasimhan ◽  
Mike Riseborough
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Condition Based Maintenance ◽  
Degradation Modeling

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

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Ali Ashasi-Sorkhabi ◽  
Stanley Fong ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Health Management ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Vibration Data ◽  
Condition Indicators ◽  
Failure Data ◽  
Wide Range

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.

Fault detection algorithms for real-time diagnosis in large-scale systems

2001 ◽  
Author(s):  
Thiagalingam Kirubarajan ◽  
Venkatesh N. Malepati ◽  
Somnath Deb ◽  
Jie Ying
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Large Scale ◽  
Large Scale Systems ◽  
Detection Algorithms ◽  
Time Diagnosis

scholarly journals Real Time Fault Detection in Photovoltaic Systems

2017 ◽  
Vol 111 ◽  
pp. 914-923 ◽  
Author(s):  
Mohamed Hassan Ali ◽  
Abdelhamid Rabhi ◽  
Ahmed El Hajjaji ◽  
Giuseppe M. Tina
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Photovoltaic Systems
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