A novel adaptive fault diagnosis algorithm for multi-machine equipment: application in bearing and diesel engine. (May 2023)

• Record Type: Journal Article
• Authors: Liu, Yangshuo; Kang, Jianshe; Bai, Yunjie; Guo, Chiming
• Is Part Of: Structural health monitoring. Volume 22:Number 3(2023)
• Page Start: 1677
• Page End: 1707
• Publication Date: 2023-05
• Subjects: Fault diagnosis -- condition monitoring -- model adaptability -- adaptive noise reduction -- feature sensitivity analysis -- feature alignment -- sparse autoencoder -- surrogate model -- hyperparameter optimization; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217221113323
• Languages: English
• ISSNs: 1475-9217

Abstract

This paper proposes an adaptive fault diagnosis algorithm based on vibration signals for fault diagnosis of bearings and diesel engines. First, the improved nonlinear gray wolf optimization algorithm (NGWO) is adopted to optimize the key parameter for variational mode decomposition (VMD) with the power spectral entropy as the fitness value. Meanwhile, adaptive noise reduction of the signal is realized. Then, sensitive fault features of bearings and diesel engines are selected through a feature sensitivity analysis on the vibration signals. Also, a single-layer sparse autoencoder is used to align the feature dimensions of each type of data to construct feature matrix samples. Subsequently, a deep neural network (DNN) consisting of a two-layer stacked sparse autoencoder (SSAE) and a Softmax classification layer is constructed to realize failure mode recognition. During the training process of DNN, a surrogate model formed by NGWO and a back propagation neural network is employed to optimize the hyperparameters of SSAE. Finally, to verify the effectiveness of the proposed fault diagnosis algorithm, fault diagnosis experiments are conducted on the fault data set of bearings and diesel engines. The diagnosis results show that the proposed method achieves high-precision fault diagnosis for bearings and diesel engines and performs stably for small samples.