Existing fault diagnosis methods for rolling bearings are mostly applied in static domains and cannot adapt to continuous and dynamic industrial data in practice. In domain incremental scenarios, achieving continuous learning and avoiding catastrophic forgetting is a major challenge for fault diagnosis. In this article, a novel domain incremental learning method based on filtering columns and knowledge base (F&K-DIL) is proposed to achieve bearing fault diagnosis in dynamic domains. It consists of filtering columns, knowledge bases, and parameter update modules. In the filtering columns, two L1-norm are used to select representative samples, which can protect previously learned knowledge in each domain while learning new knowledge. The correctness of knowledge delivery is guaranteed. Sparsity parameters that can adjust the number of representative samples flexibly are introduced to improve storage efficiency. Representative samples in different domains are stored in the knowledge base to realize knowledge sharing. In the parameter update module, cross entropy and elastic weight consolidation loss function are used to realize constraints on important parameters in old domains. It can further avoid catastrophic forgetting. Through experiments on three rotating machinery datasets, F&K-DIL is used for fault diagnosis in domain incremental scenarios with an accuracy of up to 99.6% and 37% improvement in memory rate.

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

Generalization ability is a critical property for practical motor fault diagnosis (FD). By converting time-series to images, several studies have made certain achievements. However, they still have following limitations. First, multisensor information fusion is rarely considered. Second, it is time consuming. To deal with the abovementioned problems, a multisensor-driven FD method based on visual features is proposed. Specifically, a color symmetrized dot pattern method is newly designed to infuse three multisensor signals to image. Next, a coarse and refined diagnosis framework is designed. In the coarse part, the color histogram features and a support vector machine (SVM) are utilized, and a threshold is selected to decide the coarse diagnostic samples. In the refined part, the gist (GIST) descriptor and another SVM are used to diagnose remaining samples. The results on induction motor and permanent magnet synchronous motor show that the proposed method achieved reliable diagnosis with relatively efficiency, and can generalize to different working conditions and noise.

Fault prediction of electromechanical equipment can greatly reduce its maintenance cost and prevent catastrophic damage. In order to realize the accurate fault prediction of electromechanical equipment, a fault prediction method based on spatial-temporal graph information is proposed in this article. In the proposed method, the signal data of each intermittent monitoring period are expressed by Markov field graph information, and the spatial-temporal correlation features of graph information are extracted and studied by multivariate spatial-temporal graph neural networks. The effectiveness of Markov graph information for different state expressions is demonstrated by motor fault data from the motor fault experimental platform, and the bearing fault prognostics data is used to demonstrate the feasibility and accuracy of the proposed fault prediction method. The results show that based on the local spatial state information and global time correlation information of monitoring signals, this method could accomplish accurate long-term and short-term fault prediction tasks, respectively.

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

During the long-term operation of motors, their working conditions are changing due to the industrial demands or declining health status, and traditional diagnosis methods perform poorly in that case. This article proposes a fault diagnosis method based on graph cardinality preserved attention network (GCPAT), which can work under varying working conditions, and can be generalized to the transient state. Diagnosis results are obtained by analyzing signal-converting graphs, which are composed of nodes and edges. First, the vibration signals are converted into symmetrical snowflake images by symmetrized dot pattern (SDP) method. Second, SLIC is developed to make homogeneous super-pixels in SDP images as nodes, and form graphs according to color, texture, and distance features. Finally, the GCPAT is utilized to distinguish motor status. Compared with other state-of-art methods, the results show the out-performance of GCPAT under varying working conditions both in steady and transient state.