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.

Intelligent fault diagnosis algorithms require large amounts of data to train models, and the fusion of heterogeneous information from multiple sensors increases the computational complexity exponentially. To address these problems, a few-shot cross-domain motor fault diagnosis method based on multi-sensor information fusion and meta-learning is proposed. Firstly, a multi-sensor heterogeneous information fusion framework, named low-pass pyramidal ratio-color symmetric dot pattern (RP-CSDP), is proposed. It enables to achieve the information fusion of three-axis vibration sensor and three-phase current sensor without increasing the computational burden of the intelligent diagnosis algorithm. Secondly, RP-CSDP fuses and reconstructs the data from both types of sensors into color images. Based on this, a meta-learning database is constructed. The Relation Network (RN) is improved, and various cross-working conditions and few-shot experiments are set up. Finally, the proposed method is promoted to diagnose motor faults that are not present in the training phase. The results show that the proposed method can be quickly adapted to new tasks without repeating training network when faced with new working conditions and faulty types with limited training samples.

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.

In traditional one-dimensional signal-based fault diagnosis methods, there are difficulties in signal feature extraction and complexities in adjusting model hyperparameters. Therefore, a demagnetization fault diagnosis method for permanent magnet synchronous motor (PMSM) using custom phase space reconstruction (CPSR) image is proposed, which can be generalized to condition recognition. First, a CPSR method is proposed to reconstruct the magnetic leakage signal into a two-dimensional data image. Second, to overcome the problem of poor diagnosability of high-dimensional shallow features in existing local feature extraction methods, an image feature coding method is proposed, which can improve diagnostic performance through deep feature extraction and coding. Third, the improved autonomous learning multi-model constructs different fault data clouds with coded information, and realizes fault diagnosis according to the fuzzy rules of data cloud. Four kinds of demagnetized fault motors were manufactured for experimental verification. The extensive experimental results demonstrate that the proposed method is effective.

Domain adaptation-based methods have been widely developed for fault diagnosis. However, the existing approaches mainly focus on the global distribution alignment of single-to-single domain without considering multiple scenarios, and overlooking the alignment of subdomains, which causes misclassification near the class boundaries. Thus, a multisource unsupervised subdomain adaptation network is proposed in this paper to solve fault diagnosis of rotary machines under multiple and variable working conditions. Using data images as inputs, a domain feature extractor is constructed to extract the domain-invariant features and map each source-target pair into the advanced feature space. Multi-kernel local maximum mean discrepancy is introduced to align subdomains. Moreover, the domain-specific classifiers are applied to diagnose fault category, and diagnosis result is jointly determined by multiple classifiers through weighted decision-making strategy. The experiment results on two sets of rotary machines achieve average accuracy of 98.01% and 96.04%, respectively, which demonstrates the validity of the proposed method.

The motor fault detection based on data-driven approach has achieved some results. However, there are still some problems, such as complex signal processing, long training time and poor adaptability to different working conditions. A new fault detection method for permanent magnet synchronous motor based on image content retrieval is proposed in this paper, which does not require complex signal processing and model training, and has strong adaptability to conditions. First, the fault signals are converted into images by the symmetrized dot pattern method. Second, an image coding technology based on KAZE feature and bag of features is proposed to reduce the computational cost. Then, to improve adaptability of conditions, a cyclic update algorithm of image retrieval library based on image fusion is proposed. Finally, fault detection is realized through image retrieval of feature code matching. Extensive experiments demonstrate the superiority and effectiveness of the proposed method.