Deep Learning for Fault Diagnosis based on short-time Fourier transform

The rapid advancements of the Internet of Things (IoT) enables maintenance strategies to be applied everyday to all sectors, IoT based health management plays an important role For producing quickly, with high quality while decreasing the risk of production break due to a machine stop, it is necessary to maintain the equipment in a good operational condition. This requirement can be satisfied by the implementation of maintenance strategies for faults detection. In this paper, a novel method called deep learning based on Short-Time Fourier Transform (STFT) is developed for fault diagnosis. An experimental analysis is carried out using a dataset under different operating conditions of speed and loading to substantiate the utility of the proposed strategy. Also a multi-fault deep learning classifier based on STFT is constructed for different faults in this paper. Hence, the purpose is to design an automatic detection system for mechanical components defects based on supervised classification. The diagnosis accuracy assessment is carried out by conducting various experiments on acceleration signals collected from a rotating machinery under different operating conditions.