Hybrid deep transfer learning architecture for industrial fault diagnosis using Hilbert transform and DCNN-LSTM

Early-stage fault detection has become an indispensable part of modern industry to prevent potential hazards or sudden hindrances to the production process. With the advent of deep learning (DL) applications in several fields, DL models have been used to classify faults in specific environments. Uniform texture extraction has been performed using transformed-signal processing techniques and deep transfer learning (DTL) architectures in a few studies. Traditional signal processing techniques encounter difficulties in extracting distinct fault features due to the nonlinear and non-stationary nature of the time-series fault data. In this paper, a hybrid DTL architecture comprising a deep convolutional neural network and long short-term memory layers for extracting both temporal and spatial features enhanced by Hilbert transform 2D images is presented. Three standard audio sound fault datasets comprising the malfunctioning industrial machine investigation and inspection dataset, toy anomaly detection in machine operating sounds dataset, and machinery failure prevention technology bearing vibration fault dataset with various loads and noisy environments were utilized in the experimental evaluation. The proposed model with an input size of 32 x 32 achieved an average F1 score of 0.998 on the tested datasets. The implementation of transfer learning using the three benchmark datasets resulted in the highest accuracy of the proposed model and over fivefold reduction in the training epochs. In addition, the proposed model outperformed the state-of-art models in accuracy in various environments.