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 fault diagnosis field, inconsistent distribution between training and testing data, resulted from variable working conditions of rotating machinery, inevitably leads to degradation of diagnostic performance. To address this issue, this study proposes a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization based transfer learning. Specifically, different statistics-based multi-scale sample entropies are used to improve feature discriminability for different fault patterns under each working condition and enhance similarity of fault information between different working conditions. Then, based on these hand-crafted features, an improved transfer learning algorithm, referred to as balanced adaptation regularization based transfer learning, simultaneously exploring balanced distribution adaptation and balanced label propagation, is utilized to learn an adaptive classifier to perform cross-domain fault diagnosis. Finally, two public rolling bearing datasets verify that the proposed method can achieve an accurate diagnosis and outperform several existing transfer learning methods.

In mechanical fault diagnosis, the improvement of diagnosis accuracy is the ultimate aim for researchers. Discriminative features selected and extracted from vibration measurements are commonly used to identify fault types. When a certain distinguishing characteristic is exhibited in a feature, numerous classification methods are applied for pattern recognition and diagnosis decisions. However, it is difficult to conduct accurate diagnosis if the distinguishing ability of the extracted or optimized feature remains poor. To this end, this paper proposes a new approach for classifying concurrent faults in rotating machinery, based on Bayesian discriminating analysis and time series analysis, which can solve the problem of there being no characteristic parameter that can provide a degree of discrimination information for concurrent fault types. In order to demonstrate the validity of the proposed methodology, several experiments on concurrent fault diagnosis are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of concurrent fault diagnosis.

Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty in extracting the weak features of the signal under variable load conditions, GCN is not suitable for deep neural network (DNN), and the receptive scale of GCN is unknown that limits the application of GCN in machine fault diagnosis. To address these issues, a multi-scale cluster-graph convolution neural network with multi-channel residual network (MR-MCGCN) is proposed for machine fault diagnosis in this article. First, multi-channel residual network (MCRN) is proposed for extracting the weak feature in the signal. Then, the finite graph data of signal and different scales are generated by the autoencoder (AE) graph generation layer. Finally, a multi-scale cluster-graph convolution neural network is proposed for achieving intelligent fault diagnosis. Also, the three different datasets are used for verifying the effectiveness of the proposed MR-MCGCN. The experimental results show that the proposed MR-MCGNN can achieve the highest diagnosis results than other methods even under variable load conditions.

Machinery fault diagnosis methods based on dimensionless indicators have long been studied. However, traditional dimensionless indicators usually suffer a low diagnostic accuracy for mechanical components. Toward this end, an effective fault diagnosis method based on redefined dimensionless indicators (RDIs) and minimum redundancy maximum relevance (mRMR) is proposed to identify the health conditions of mechanical components. In the proposed method, the vibration signals are first processed by the variational mode decomposition, and multiple RDIs are constructed based on the decomposed signals. Subsequently, the mRMR approach is introduced to select the RDIs and several important RDIs can be obtained. Finally, the obtained RDIs are fed into a grid search support vector machine to perform fault pattern identification. To verify the superiority of the proposed method, two experimental examples for different fault types of mechanical components including rolling bearing and gearbox are conducted. The experimental results demonstrated that the RDIs as new fault features can effectively solve the deficiency of the traditional dimensionless indicator, and has stronger distinguishing ability for machinery faults. Additionally, our proposed method successfully differentiated 12 fault conditions of rolling bearings and nine fault conditions of gears with average accuracies of 97.47% and 97.12% with 11 and 5 RDIs, respectively.