Remaining Useful Life Predictions for Bearings Using Spectrogram and Scalogram-Based Convolutional Neural Networks

Abstract

Bearings are critical in today’s mechanisms, and their reliability is continuously improving. Yet, working under high loads for long periods, bearings will degrade and eventually fail. An unpredicted bearing failure can lead to total and catastrophic failures of machines and may even lead to human injuries that result in substantial economic losses and reductions in production. Determining a bearing’s remaining useful life (RUL) has become an important topic in many industrial fields. Vibration signals are the most used representation for understanding a bearing’s health status. Using different algorithms, time-domain vibration signals can be transformed into time-frequency domain signals that help indicate a bearing’s status. For instance, this thesis investigates spectrograms and scalograms to visually represent a bearing’s health condition using a short-time Fourier transform (STFT) and a continuous wavelet transform (CWT). Both representations are plotted as a function of time and frequency and can detect the bearing’s working condition. However, spectrograms are advantageous in revealing frequency changes along the time axis, while scalograms facilitate the detection of abrupt changes. Combined with a convolutional neural network (CNN), these plots can be used to interpret bearing RUL. The strength of CNNs lie in their ability to identify and detect features in images, including such tasks as image classification, using share-weight architectures, convolutional layers, and kernels. This thesis explores CNNs combined with spectrograms and scalograms using the PRONOSTIA dataset to perform bearing RUL predictions and explore relationships between prognosis and diagnosis for bearing faults analysis.