A wavelet neural network informed by time-domain signal preprocessing for bearing remaining useful life prediction

Rolling bearings are important components in rotating machinery in various industries. Conducting intelligent prognostics for bearing remaining useful life (RUL) prediction plays an important role in the health management and reliability assessment of these machinery systems. While the bearing fault prognosis using measured system response through machine learning techniques has attracted significant attention and demonstrated promising potential, completely data-driven approaches face some challenges in understanding complex domains with data efficient learning. In this research, we develop an integral bearing fault prognosis framework informed by the well-designed time-domain signal preprocessing to conduct the bearing RUL prediction. This framework is built upon physical feature-oriented signal preprocessing and an associated wavelet neural network (WNN). Sequential procedures of time-domain analyses are proposed to extract the physical features of bearing degradation. Empirical mode decomposition (EMD) is specifically chosen owing to its capability of handling bearing fault signals that are nonstationary with underlying nonlinearities. The WNN is a new class of neural networks that combines the classic neural network and wavelet analysis. Here in this research a WNN model built upon B-spline mother wavelet to suit the preceding EMD-based signal preprocessing is constructed to process the bearing degradation features extracted and then identify their correlation with bearing RUL. The combination of EMD-based signal preprocessing and B-spline mother wavelet in WNN enables the network to learn the input-output correlation in a physical sense. Case studies formulated upon multiple bearing datasets and multiple benchmark methods are carried out to systematically validate the proposed framework. The results consistently demonstrate prediction accuracy and performance robustness.