Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (19), p.7330
• Main Authors: Liu, Yiyang, Li, Fei, Guan, Qingbo, Zhao, Yang, Yan, Shuaihua
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Bearings; Classification; Deep learning; Dense Residual Networks; Energy spectra; energy spectrum feature map; Fault detection; Fault diagnosis; Feature maps; Industrial production; Lightweight; Machinery; Methods; Neural networks; power equipment; Roller bearings; Sensors; smart grid; transfer learning; Wavelet transforms; Work environment; Working conditions
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197330

With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect.