Fault diagnosis has received considerable attention because its implementation can effectively prevent costly and even catastrophic downtime. However, quickly identifying faults and accurately obtaining diagnosis results from a feature set of rotating machinery are still a problem. To this end, this paper proposes an effective method based on a weighted extreme learning machine (WELM) with wavelet packet decomposition (WPD) and kernel principal component analysis (KPCA). The feature set affecting classification accuracy can be obtained using WPD and KPCA. By taking feature reliability into consideration, a new type of improvement to the extreme learning machine (ELM), i.e., WELM, is proposed by associating the hidden layer and input layer with a weight matrix. The WELM model can help in guaranteeing a quick and an accurate identification of fault status. To verify the superiority of the fault identification speed and accuracy of the proposed method, results from other methods, namely, using the sensitive features based on WPD and KPCA with ELM, a back-propagation neural network, and a support vector machine, were compared. The experimental results indicate that the proposed method can effectively improve the accuracy and quickly diagnose the fault. The average accuracy of fault classification could reach 95.45%, and the computation time of WELM was only 0.0156 s.

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D‐CNN for rolling bearing fault diagnosis. First, 1‐dimension convolutional neural network (1D‐CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross‐entropy loss function and Adam optimizer are used to minimize the classification errors and the second‐order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

In real industry, due to changes in operating conditions and differences in systems of interest, domain shift is a common problem, which results in the degradation of the diagnostic performance. Moreover, insufficient labeled or unlabeled samples greatly limit the adaptability of fault diagnosis methods. To solve these problems, the latest domain adversarial transfer learning techniques are studied. However, most of the existing studies just reduce the distribution discrepancy of two domains with fixed distance metrics, which cannot adapt well to distribution variation during testing. This article proposes a new deep transfer learning method based on Wasserstein generative adversarial networks (WGAN) and minimum singular value for non-nomologous bearings, where the training samples and the testing samples are from related not the same machinery. In the proposed method, a domain critic network is adopted to provide the discrepancy metric for improving domain adaptation ability. In addition, the minimum singular value is used to capture effective categories information measurement in the classifier training process. The proposed method is verified by two different bearing datasets from Case Western Reserve University and the Intelligent Maintenance System. The experiments indicated that the proposed method can achieve superior performance over other existing methods in terms of diagnosis accuracy.

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

In underdetermined blind source separation (UBSS) of vibration signals, the estimation of the mixing matrix is often affected by noise and by the type of the used clustering algorithm. A novel UBSS method for the analysis of vibration signals, aiming to address the problem of the inaccurate estimation of the mixing matrix owing to noise and choice of the clustering method, is proposed here. The proposed algorithm is based on the modified k-means clustering algorithm and the Laplace potential function. First, the largest distance between data points is used to initialize the cluster centroid locations, and then the mean distance between clustering centroids average distance range of data points is used for updating the locations of cluster centroids. Next, the Laplace potential function that uses a global similarity criterion is applied to fine-tune the cluster centroid locations. Normalized mean squared error and deviation angle measures were used to assess the accuracy of the estimation of the mixing matrix. Bearing vibration data from Case Western Reserve University and our experimental platform were used to analyze the performance of the developed algorithm. Results of this analysis suggest that this proposed method can estimate the mixing matrix more effectively, compared with existing methods.

Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even impossible to obtain. In this study, we addressed a challenging few-shot bearing fault diagnosis problem with few or no training labeled samples of novel categories. To tackle this problem, we considered a semi-supervised prototype network based on few-shot bearing fault diagnosis with pseudo-labels. The existing prototypical networks with pseudo-label methods train a pseudo label model to label unlabeled samples using high-dimensional labeled data, which cannot eliminate the instability of the pseudo-label model caused by dimensional labeled features. To mitigate this issue, we used kernel principal component analysis to reduce the dimensions of and remove redundant information from high-dimensional data. Specifically, we used the pseudo-label prediction algorithm with probability distance to label unlabeled samples, aiming to improve the labeling accuracy. We applied two well-known bearing data sets for the validation experiments with symmetry parameters. The findings illustrated that the classification accuracy of the proposed method is higher than that of other existing methods.