Digital-Twin-Driven PMSM Inter-Turn Short-Circuit Fault Diagnosis Method

Intelligent fault diagnosis methods based on deep learning have attracted significant attention in recent years. However, it still faces many challenges, including complex and variable working conditions, noise interference, and insufficient valid data samples. Therefore, a novel deep transfer learning bearing fault diagnosis model is designed in this work by fusing time-frequency analysis, residual network (ResNet) and self-attention mechanism (SAM). A multiscale time-frequency feature map (MTFFM) and global statistical feature matrix (GSFM) of vibration signals are first constructed using wavelet packet transform (WPT). A deep feature extraction network combining ResNet and SAM networks is then designed to realize the fused extraction of local and global time-frequency features. Finally, we construct a joint loss function by the combination of multi-kernel maximum mean discrepancy (MK-MMD) and the domain adversarial neural network (DANN) to optimize the depth feature extraction network, which improves the cross-domain invariance and fault state discrimination of depth features. The proposed optimization method fully exploits the advantages of high-dimensional spatial distribution difference evaluation and gradient inversion adversarial strategy. Its effectiveness is demonstrated through variable working condition transfer fault diagnosis tasks, showing superior performance compared with other intelligent fault diagnosis methods.

Fault diagnosis transfer learning models commonly employ deep neural networks (DNNs) to analyze time–frequency features. However, excessively DNNs can result in diminished generalization capabilities, leading to subpar performance of the model across various working conditions. Furthermore, inappropriate domain adaptation (DA) strategies significantly constrain the accuracy of the model. To address this issue, a robustly optimized residual-network and vision transformer (ViT) domain adaptation model is proposed in this article, combining wavelet packet transform (WPT), residual networks, and self-attention mechanisms. Firstly, the WPT is utilized to construct multi-band wavelet coefficient matrix (MWCM) and corresponding multi-band wavelet coefficient time–frequency feature matrix (MWSM) with small size and feature aggregation. Subsequently, a shallow robustly optimized residual network is designed to effectively extract features from MWCM, considering the spatial distance dependencies of features. Additionally, ViT is employed for time–frequency global feature extraction from MWSM. Furthermore, domain adversarial neural network and multi-kernel maximum mean discrepancy are employed to extract domain-invariant features from signals of different operating conditions and fault types. At last, three fault diagnosis experiments are conducted in multi-condition scenarios of bearings. The experimental results illustrate the superiority and effectiveness of the proposed model.

The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method.

Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faulty position and severity of the inter-turn short-circuit (ITSC) fault, which is one of the most destructive and frequent faults in PMSM. Compared with the existing research results gained by particle swarm optimization algorithms, in this paper, the methods using other optimization algorithms incorporating genetic algorithm, whale optimization algorithm and stochastic parallel gradient descent algorithm (SPGD) can acquire more stable and precise results. In particular, the method based on SPGD can obtain the most desirable performance among the methods mentioned above; that is, the relative error of short-circuit turns ratio is approximately as low as 0.03%. In addition, in the case of asymmetric input three-phase voltage and with the adverse impact of high-order harmonics at different load moments, the fault diagnosis method based on SPGD still maintains relatively satisfactory properties. Finally, the verification on the actual PMSM platform demonstrates that the SPGD can still diagnose the faulty severity.

Modeling and fault diagnosis of multi-phase winding inter-turn short circuit for five-phase PMSM based on improved trust region

The stator winding inter-turn short fault of permanent magnet synchronous motor (PMSM) is a common motor fault with high probability. If this fault is not detected and handled in time, small initial fault will rapidly develop into other faults such as grounding short, inter-phase short, winding short et al., and even the motor damage. In order to diagnose the inter-turn short fault of PMSM, this paper firstly builds the model of the inter-turn short fault of PMSM, and then proposes a fast and simple fault diagnosis method for extracting the features of the inter-turn short fault under noise. For the first time, by introducing the full-short-circuit resistance Rf that relates with the fault severity factor δ and by estimating the inductance of inter-turn short fault motor on-line according to the three principles of the inductance calculation, which is used in the inter-turn short fault of transformer winding, the inter-turn short analysis model of PMSM is deduced. The model can be used to simulate the performance not only the health of PMSM but also the fault of inter-turn short fault of PMSM in one phase winding. This paper analyses the transient and steady state performance of 3-phase unbalanced current, speed and electromagnetic torque ripple and the relationship between full-short-circuit resistance and short current under fault condition. Finally, a fast and simple on-line fault diagnosis method of PMSM with inter-turn short fault is proposed based on phase sensitive detection (PSD) algorithm. The method uses the fundamental and third harmonic features of inter-turn short fault current. The simulation results show that the model can be well suited for the simulation of PMSM with inter-turn short fault, and the method can effectively extract the features and diagnose the fault.

The permanent magnet motor mainly uses the permanent magnet excitation, the motor fault will affect the excitation performance of the permanent magnet, thus affecting the performance of the entire motor, serious stator inter-turn short circuit fault will cause short circuit between phases, causing three-phase current imbalance, resulting in increased harmonic magnetic field, rotor temperature rise. In this paper, the 5kW permanent magnet synchronous motor in 200mm shaftless rimless thruster is taken as the research object, and the single-phase two-slot inter-turn short circuit and two-phase inter-turn short circuit are analyzed. The Ansoft finite element analysis software is used to analyze the influence on the motor by setting two kinds of winding faults, which provides a reference for the fault diagnosis of synchronous motor.

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time–frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time–frequency format. Subsequently, a CNN model is developed to extract and classify these time–frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.

Current methods for bearing fault diagnosis often fall short in addressing data privacy concerns and typically rely on one-to-one transfer strategies, which are inadequate for achieving knowledge transfer in distributed environments. To address this issue, a distributed fault diagnosis method for rolling bearings based on federated transfer learning is proposed. This method ensures data privacy while integrating fault knowledge from multiple domains, thereby enabling more efficient knowledge transfer. Specifically, a domain adversarial neural network (DANN) is introduced as the base model within the federated learning framework. Additionally, maximum mean discrepancy (MMD) is incorporated into the DANN to enhance the transfer of fault knowledge. Finally, a dynamic weighting parameter update method based on MMD is designed to evaluate the feature discrepancies between source and target domains, thereby updating the parameters of the federated framework and achieving global model aggregation. Experimental results on two bearing datasets demonstrate that the proposed method excels in both distribution alignment and fault diagnosis.

Fault detection is an important issue for permanent magnet synchronous motors (PMSMs). In the initial stage, it is very crucial to detect stator winding inter-turn short-circuit failure, which is one of the most common types of faults. In this paper, a new approach based on electromechanical torque has been proposed to detect the stator inter-turn short circuit fault (ISCF) that occurs in surface-mounted permanent magnet synchronous motors (PMSMs). New fault signatures based on the torque signal that can be used in stator winding ISCF detection are tried to be found in the torque frequency distribution. Fast Fourier Transform (FFT) was used to extract the torque frequency components associated with the stator ISCF. It was found that the amplitudes of the 2nd and 4th harmonic components of the torque signal are distinctive features that can be used for stator winding ISCF detection in PMSM. With the proposed components of the 2nd and 4th harmonic of torque, an inter-turn fault can be easily detected at the initial stage. Both experimental results and simulation results for healthy and three different faulty states (2%, 12.5%, and 25% ISCF) at different load levels and different speeds are presented in this paper.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Intelligent Fault Diagnosis Based on Receptive Field of DCNN for Rotary Machine under Variable Conditions

Inter-Turn Short Circuit (ITSC) faults in stator winding of Permanent Magnet Synchronous Motors (PMSMs) is one of the major cause of motor failure. This paper deals with the diagnosis of ITSC faults in PMSM tacking into account of all space harmonics. To do this, it's necessary to build a mathematical model of the machine object of the diagnosis. This model must be reliable and able to describe the behavior of the machine in deferent operation modes. Winding Function Approach (WFA) is among the most significant modeling methods because it takes into account the real distribution of the windings in the stator slots. To highlight the interest of the model employed and the efficiency of the spectral analysis technique, a series of simulations are performed in both cases of operation: healthy and in the presence of ITSC faults.

This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to different environmental and operational conditions. To solve these problems, robust current signal imaging method and deep learning-based feature extraction method are developed. The overall procedure includes the following three key steps: (1) transformation of a one-dimensional time-series current signal to a two-dimensional image, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The proposed RP method develops from feature engineering that provides the dominant fault feature representations in a robust way. The proposed RP is designed that maximizes the features of inter-turn short fault and minimizes the effect of noise from systems with various capacities. To demonstrate the validity of the proposed method, two case studies are conducted using an artificial fault seeded testbed with two different capacities of motor. By calculating the feature using only the electrical current signal of the motor without the parameters related to the capacity of the motor, the proposed feature can be applied to motors with different capacities while maintaining the same performance.