Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 100, Issue -, Pages 152-166Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.07.032
Keywords
Diagnostics; Gearbox; Discrepancy analysis; Fluctuating operating conditions; Hidden Markov Model; Probabilistic techniques
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Funding
- Eskom Power Plant Engineering Institute (EPPEI)
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In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimistd machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology. (C) 2017 Elsevier Ltd. All rights reserved.
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