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.
Aiming at the problems of complex diesel engine cylinder head signals, difficulty in extracting fault information, and existing deep learning fault diagnosis algorithms with many training parameters, high time cost, and high data volume requirements, a small-sample transfer learning fault diagnosis algorithm is proposed in this article. First, the fault vibration signal of the diesel engine is converted into a three-channel red green blue (RGB) short-time Fourier transform time–frequency diagram, which reduces the randomness of artificially extracted features. Then, for the problem of slow network training and large sample size requirements, the AlexNet convolutional network and the ResNet-18 convolutional network are fine-tuned on the diesel engine time–frequency map samples as pre-training models with the transfer diagnosis strategy. In addition, to improve the training effect of the network, a surrogate model is introduced to autonomously optimize the hyperparameters of the network. Experiments show that, when compared to other commonly used methods, the transfer fault diagnosis algorithm proposed in this article can obtain high classification accuracy in the diagnosis of diesel engines while maintaining very stable performance under the condition of small samples.
To more accurately evaluate the health state of rolling bearings, this paper proposes a health status evaluation method based on empirical pattern decomposition, genetic algorithm and BP neural network. Firstly, the vibration signal is decomposed by empirical mode decomposition (EMD) and the time domain features of each intrinsic mode function (IMF) component are extracted, and the signal‐to‐noise ratio (Snr) of the signal is improved effectively. Then, the initial threshold and weight of BP neural network are optimized by genetic algorithm, which effectively improves the Snr of the signal. Finally, the extracted features are input into the optimized BP neural network to realize the identification of different states of the bearing. The effectiveness of the method has been effectively verified in the bearing data of Case Western Reserve University bearing dataset and it has higher accuracy and robustness than other common evaluation methods.
The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining adaptive multi-threshold segmentation and subdomain adaptation. First of all, in the data acquisition stage, a non-contact, easy-to-arrange, and low-cost sound pressure sensor is used to collect equipment signals, which effectively solves the problems of contact installation limitations and increasingly strict layout requirements faced by traditional vibration signal-based methods. The continuous wavelet transform (CWT) is then used to convert the original vibration signal of the device into time–frequency image samples. Further, to highlight the target fault characteristics of the samples, the gray wolf optimization algorithm (GWO) is combined with symmetric cross entropy (SCE) to perform adaptive multi-threshold segmentation on the image samples. A convolutional neural network (CNN) is then used to extract the common features of the source domain samples and the target domain samples. Additionally, the local maximum mean discrepancy (LMMD) is introduced into the parameter space of the deep fully connected layer of the network to align the sub-field edge distribution of deep features so as to reduce the distribution difference of sub-class fault features under different working conditions and improve the diagnostic accuracy of the model. Finally, to verify the effectiveness of the proposed diagnosis method, a fault preset experiment of the gearbox under variable speed conditions is carried out. The results show that compared to other diagnostic methods, the method in this paper has higher diagnostic accuracy and superiority.
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