The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis (FDD) of the motor drive system. This article reviews the application of AI techniques in motor FDD in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps, and future challenges in fault monitoring and diagnosis of motor faults are discussed.

Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

Electric powertrain used in electric vehicles (EVs), which is constituted by motor, transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review contributes to comprehensively summarizing artificial intelligence (AI)-based/AI-supported approaches in EV powertrain condition monitoring and fault diagnosis that can be used in EV applications. The application of AI on PE in EV is a new attempt, which can solve many issues with better performance than traditional methods, and even achieve functions that the conventional methods cannot achieve. This paper thoroughly discusses the motivation, advantages, limitations, and challenges associated with AI-supported methods through case summaries, classification, comparisons, and quantitative analyses between conventional and AI-based approaches. Furthermore, the review concludes by proposing forward-looking future trends in this field.

Bearing, as a vital component in electric powertrains, is increasingly used globally such as in electric vehicle (EV). Their damages and faults may bring huge cost loss to the industry and even threaten personal safety. This paper proposes an inferable deep distilled attention network (IDDAN) method which is a self-attention mechanism and transfer learning-based method to diagnose and classify multiple bearing faults in various motor drive systems efficiently and accurately. Compared with convolutional networks, the self-attention-based network can better extract the global feature information and easier to benefit from large amounts of pre-training data. Its significance is to accurately classify various faults of the target machine when the labeled data of the target machine is not enough to directly train the diagnosis model. Firstly, this paper attempt to apply the self-attention-based network to build an advanced fault diagnosis model. Secondly, this paper optimizes the structure of networks through knowledge distillation (KD) technique to require a lighter and fast model. Thirdly, this paper proposes a new data augmentation strategy for 1-D vibration signals to provide large-scale pre-training samples for IDDAN. Experiments show that the self-attention mechanism-based model is more likely to benefit from large-scale data. After testing, compared with many methods and other exist similar methods, the proposed method achieves higher classification accuracy and better performance.

To eliminate the side impacts of inductance mismatch on the accuracy of finite control set model predictive current control (FCS-MPCC) scheme used for surface-mounted permanent magnet synchronous motors, this letter proposes a new flux observation transfer-based inductance identification strategy. First, a flux observer based on sliding mode theory is developed, with its stability analyzed. Second, the flux estimation errors are discussed, obtaining the relationship between the estimated flux and the inductance variations. On this basis, the inductance is detected using the flux information. Then, the estimated inductance is used to achieve the precise FCS-MPCC method. Finally, experiment is conducted on a three-phase SPMSM prototype to validate the proposed techniques.

Bearings are vital components of motor drive systems and are widely used in various industrial applications. Bearing failures can lead to system collapse and pose a risk to human safety. Therefore, real-time monitoring and diagnosis of multi-fault bearings are crucial. This paper proposes a Mel-spectrum-based contrastive self-supervised Transformer (Mel-CSST) model to efficiently detect multiple bearing faults in electric motors through vibration noise signals. Among them, the contrastive self-supervised Transformer model (CSST) can be pre-trained without the need for labeled data, significantly improving the fault detection accuracy of the target bearing after transfer learning using the parameter-frozen domain-adversarial (PFDA) method. Mel-spectrums are converted from a mass of sub-signals generated by the random-masked sliding window (RMSW) method, providing training data sample pairs for the CSST model. Mel-spectrums can analyze significant vibration noise signals at lower frequencies in more detail, revealing the fault features missed by the standard fast Fourier transform. Furthermore, the encoder part of Mel-CSST uses a modified Transformer network to ensure the feature extraction effectiveness of CSST. The proposed method can be easily transferred to be used on target bearings without expensive labelling data in practical applications. Experiments using two real bearing datasets measured from two test rigs, along with comparison experiments with other existing methods, validate the effectiveness of the proposed method.