Multi-fault diagnosis of industrial rotating machines using advanced sliding window and WSST-CNN

Accurate fault diagnosis of rotating machines is the most important part of industries, as it helps manage the workforce and inventory within the stipulated time. In industries, when the fault occurs at one component, that may propagate to other components due to excessive speed and load, leading to the occurrence of a simultaneous fault condition. Identification of multi-component faults is relatively more difficult than single-component faults due to the possibility of characteristic frequency overlapping that is observed during signal processing. This work considered various single and complex multi-component fault conditions of a rotating machine, and data is acquired at different speed and load conditions using a standard acoustic array setup. The signals are then converted into signal segments using a continuously moving advanced sliding window. From the extracted segments time-frequency spectrums are generated by using Wavelet Synchrosqueezed Transform (WSST) followed by the training of deep neural networks. The performance analysis of the model is carried out, and it is found that the proposed methodology helped diagnose multi-faults at different speed and load conditions. It mitigates the complexity of high-end signal processing techniques for distinguishing the fault characteristics. This study will be beneficial for industries to diagnose multi-faults in incipiently.