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Fault diagnosis of the planetary gearbox (PGB) of wind turbines (WTs) plays an important role in the normal operation of WTs. Current studies commonly focus on the diagnosis of fault types of WT PGBs. Nevertheless, in addition to identifying the fault type, the current severity of the fault is also instructive for the maintenance and repair of WT PGBs. Thus, a novel optimized stacked diagnosis structure (OSDS) is proposed for the identification of fault type and severity. Compressed sensing is adopted to implement the compressed sampling of original vibration signals collected by the wireless sensor. Then, the compressed samples are input into first- and second-layer deep belief networks (DBNs) for the separate identification of fault type and severity. In order to realize the best feature extraction performance of DBNs, every single DBN in the OSDS is optimized with the chaotic quantum particle swarm optimization (CQPSO) algorithm. For OSDS, which is a hierarchical diagnosis system, the misdiagnosis results of the first layer will bring irreversible influence to the diagnosis of the second layer. That is to say, an incorrect fault type diagnosis will mean that these signals are wrongly classified, making them unable to judge the severity of the fault. Because the first-layer DBN is optimized with PGB historical data and the CQPSO algorithm, it shows an excellent performance in identifying fault types. Therefore, the diagnostic performance of OSDS has not been affected by the absence of diagnosis, and still shows an excellent recognition performance of fault type and severity in the experiment. This verifies its excellent role in the fault diagnosis of WT PGBs.

In recent years, the variational mode decomposition (VMD) method has been introduced for rotating machinery fault diagnosis. However, the results largely depend on its parameters. When an optimization algorithm is employed to optimize these parameters, the fitness function is critical. In this paper, a new fitness function, envelope sample entropy, is constructed. Based on this, a whale optimized VMD method is proposed for rotating machinery fault diagnosis. First, the vibration signals were decomposed by the optimized VMD method to obtain a series of intrinsic mode functions (IMFs), from which the IMFs containing the main information were selected. Then, features were extracted from the selected IMFs and their dimensions were reduced using the local tangent space alignment method. Finally, support vector machine was adopted for fault identification. Compared with related methods, the experiment results show that the proposed method obtains a higher fault recognition accuracy.

The application fields of piezoresistive pressure sensor in recent years have become more and more extensive. Besides, the high reliability of the sensor is also required. However, considering some sensors operate in hostile environments and need to ensure continuous operation accuracy, Prognostics and Health Management (PHM) for piezoresistive pressure sensor should not be ignored. To solve this problem, a fault diagnosis and prognostic method, which combines with Support Vector Machine (SVM) and deep gated recurrent unit network (DGRU) optimized by Hunter-prey Optimization (HPO), is proposed in this paper. First, three fault types of the sensor are defined. Second, SVM is adopted to realize the fault diagnosis. Third, two layers DGRU is employed to predict the health index which is defined to represent the health state of the sensor. Meanwhile, the optimal parameters of the DGRU are optimized by HPO algorithms. Finally, the remaining useful life (RUL) can be estimated by the predicted health index and failure threshold. The proposed method in this paper is proved to be effective and accurate. The fault diagnosis accuracy is 100% in the three fault types defined by this paper. The minimum mean absolute error is 6 ×10-5. It proves the proposed method of PHM in this paper is applicable in real application.

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