Fault classification of the rolling bearing based on minimax entropy domain adaption augmented with signal generation algorithm

Rolling bearing faults have been capturing substantial research attention as they are the root causes of malfunctions in mechatronics systems than any other factors. The detection of rolling bearing faults in the early stage is therefore a mandatory requirement demanded by reliable industrial plants. To release the dependence of diagnostic methods on human expertise and system’s understanding, this work proposes a fault classification method for rolling bearings that is based on a deep learning framework. The framework consists of a minimax entropy domain adaptation algorithm augmented with a signal generalization algorithm. The function of the signal generalization algorithm is to reduce the domain shift between training and testing datasets that are often obtained experimentally from different working conditions. The generalized signal is then represented in the form of Fourier series whose coefficients contain intrinsic information that associated with different types of bearing faults. A convolutional neural network extracts the hidden information of bearing faults buried in the Fourier coefficients and then categorises the working condition of the bearing under test. By combining the advantages of both signal processing techniques in the frequency domain and the minimax entropy domain adaptation, the novel diagnostic framework is able to detect bearing faults from different working conditions. The effectiveness of the proposed diagnostic algorithm is experimentally verified by two case studies that were prepared with different types and levels of bearing faults.