Unsupervised Health Indicator Fusing Time and Frequency Domain Information and Its Application to Remaining Useful Life Prediction

The prediction of component remaining useful life (RUL) is essential in making an appropriate maintenance plan for equipment. Constructing a reliable health indicator (HI) is crucial for RUL prediction. HI can be generated by quantifying distribution discrepancies. Most existing methods construct HIs based on the time domain, whereas in certain cases, time-domain data contain fewer degradation characteristics than frequency-domain data. To enhance the applicability and quality of HIs under different conditions, this article presents a novel unsupervised approach for generating HI from both the time and frequency domains. Considering the frequency-domain data characteristics of mechanical vibration signals, an exponential mixture model (EMM) is first applied to extract the frequency-domain distribution characteristics. Furthermore, a Gaussian mixture model (GMM) is used to mine time-domain distribution characteristics. Subsequently, a distribution contact ratio metric (DCRM) is employed to respectively generate the time and frequency domain HIs by quantifying the discrepancies between baseline distribution and data distributions at different degradation moments. The final HI is constructed by weighting the time and frequency domain HIs. RUL prediction is achieved using the Proposed-HI and a variant of recurrent neural network. Finally, the efficiency and superiority of this approach are validated using multiple gear life-cycle datasets, and the presented HI exhibits a higher RUL prediction accuracy than classical and advanced unsupervised HIs.