Vibration signal of mechanical component usually exhibits non-linear and non-stationary characteristics, the key step of fault diagnosis is to extract discriminant features hidden in the vibration signal, in order to improve diagnostic performance and identify new fault class, a novel fault diagnosis method of hidden feature label propagation based on deep convolution variational autoencoder (HFLPDCVA) is proposed. Firstly, the raw vibration signal is transformed into frequency spectrum data by using fast Fourier transform as the input of the model. Secondly, the variational autoencoder is used to construct the convolutional neural network, and the non-fixed dropout parameter is fused to change the network to improve the identification of network hidden layer features. Finally, the label propagation algorithm is applied to process the hidden features of the full connection layer, and the local common features between the unknown label samples and the known label samples are extracted to predict the class of unknown label samples, including the new emerge fault class. The effectiveness of the proposed method is verified on bearing fault data sets under variable working conditions and damage data sets of self-priming centrifugal pump under a single working condition. Experimental results show that the proposed HFLPDCVA method can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers.

In the research field of mechanical equipment fault diagnosis, usually only the existing fault types are identified, and the new emerge class of the fault is ignored, however, the new emerge fault class may also occur actually. In order to solve the problem, a novel fault diagnosis model based on deep convolution variational autoencoder network and adaptive label propagation (DCVAN-ALP) is proposed. Firstly, the initial high dimensional features are constructed by using the double tree complex wavelet packet method as the input of the model. Secondly, the convolutional neural network architecture is applied to construct the variational autoencoder, and the local and non-local characteristics of samples are embedded into the loss function for training, which is considered to improve the identification of hidden layer features of the neural network. Finally, t-SNE and the improved label propagation algorithm are adopted to process the hidden features of the neural network, which can achieve the purpose of diagnosing the existing fault class and especially new emerge fault class. Experimental results show that the proposed model can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers in diagnosing new emerge fault class.

The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual‐objective optimization function. In order to adjust the trade‐off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved Gray Wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.