A periodic anomaly detection framework based on matrix profile for condition monitoring of planetary gearboxes

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As the core power transmission component of mechanical equipment, planetary gearboxes unavoidably experience faults due to harsh working environments and continuously heavy loads. Therefore, to ensure safe operation of the equipment and reduction of maintenance costs, condition monitoring of planetary gearboxes is critical. Anomaly detection provides a potential approach to overcome the limitations of traditional signal processing technologies and deep learning methods. However, since the monitoring signal of planetary gearboxes contains various harmonics and periodic impulses, current unsupervised anomaly detection methods are difficult to achieve a satisfactory result for the monitoring signal. To overcome the above problem, a novel periodic anomaly detection framework is put forward for planetary gearboxes condition monitoring based on matrix profile (MP). In this work, a periodic anomaly detection method, namely matrix profile guided multiple residuals average (MPGMRA), is first introduced to detect periodic impulses caused by the local damage. Then, to better extract the weak periodic impulses, an adaptive parameter selection strategy based on impulse-to-noise ratio (INR) is proposed. At last, both simulated signal and experimental data from a planetary gearbox are analyzed to demonstrate the applicability of the proposed framework, and the correlation kurtosis (CK) is introduced to evaluate the magnitude of the periodic fault component. The results manifest that this framework is able to recognize the condition of planetary gearboxes successfully and the performance based on CK can be improved by 5.4 times.