A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques. (1st February 2018) - Schmidt, S.; Heyns, P.S.; de Villiers, J.P.
Highlights: A new technique is used to combine machine and operating condition information. The relevance of machine condition models is automatically inferred from the data. The technique is developed for environments with large varying operating states. The technique is used to successfully detect, isolate and trend gear damage. Abstract: 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 optimised 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.