Motor Stator Current Signal Research Articles

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

PCDC: Prototype-assisted dual-contrastive learning with depthwise separable convolutional neural network for few-shot fault diagnosis of permanent magnet synchronous motors under new operating conditions

Abstract The fault diagnosis of permanent magnet synchronous motor is of vital importance in industrial fields to ensure user safety and minimize economic losses from accidents. However, recent fault diagnosis methods, particularly the methods using deep learning, require a massive amount of labeled data, which may not be available in industrial fields. Few-shot learning has been recently applied in fault diagnosis for rotary machineries, to alleviate the data deficiency and/or to enable unseen fault diagnosis. However, two major obstacles still remain, specifically: a) the limited ability of the models to be generalized for use under new operating conditions and b) insufficient discriminative features to precisely diagnose fault types. To address these limitations, this study proposes a Prototype-assisted dual-Contrastive learning with Depthwise separable Convolutional neural network (PCDC) for few-shot fault diagnosis for permanent magnet synchronous motors under new working conditions. Operation-robust fault features are extracted to reinforce generalization of PCDC under new operating conditions by extracting fault-induced amplitude and frequency modulation features and by eliminating the influence of operating conditions from the motor stator current signals. Prototype-assisted dual-contrastive learning is proposed to clearly distinguish the fault categories even when the fault features are similar to each other by learning both local- and global-similarity features, which increases the instance-discrimination ability while alleviating an overfitting issue. Experimental results show that the proposed PCDC outperforms the comparison models in few-shot fault diagnosis tasks under new operating conditions.

An intelligent fault diagnosis method for rolling bearing using motor stator current signals

Abstract In the diagnosis of rolling bearing faults, the Motor Current Signature Analysis (MCSA) method offers advantages such as low cost, simplicity, and convenience compared to using vibration signals, temperature information, and other diagnostic objects. However, owing to the interference of high-frequency noise, power frequency, and its harmonics in current signals, which can severely affect the accuracy of bearing fault diagnosis, it is extremely challenging to use the original current signals during bearing faults directly for diagnostic purposes. Therefore, this paper proposes an intelligent fault diagnosis method based on the feature reconstruction (FR) method and convolutional neural networks (CNN). This method can achieve high-precision fault diagnosis using single-phase stator current signals from motors as the diagnostic objects. First, the FR method effectively removes the impact of high-frequency noise, supply frequency, and its harmonics from the current signals, while also highlighting subtle fault feature signals to a certain extent. Second, a CNN suitable for learning the characteristics of the current signals was constructed. Through feature extraction, learning, and classification of the current signal samples processed by the FR method, a diagnostic method with a high classification accuracy was obtained. Visualization techniques were used to present the final diagnosis results intuitively. The experimental results demonstrated the highest diagnostic accuracy and average diagnostic accuracy of the proposed method in diagnosing rolling bearing fault types, with an average diagnostic accuracy of approximately 99% for actual faulty bearing samples.

Mechanical Fault Diagnosis of a Disconnector Operating Mechanism Based on Vibration and the Motor Current

The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism.

A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map

To take full advantage of a convolutional neural network (CNN) for deep learning-based fault diagnosis, many studies have examined the transformation of sensory signals into a two-dimensional (2D) input image. An important question to consider is: how can fault-related signatures in motor stator current signals be incorporated into the 2D input image to a CNN model for fault diagnosis of a permanent magnet synchronous motor (PMSM)? To answer the question, this study newly proposes a novel health image, namely instantaneous current residual map (ICRM). Inspired by the idea that the phase and amplitude modulations in motor stator current signals are related to faulty states of a PMSM, the overall procedure for constructing ICRM includes two key steps: (1) to calculate current residuals (CRs); and (2) to spread the scaled CR pairs into a 2D matrix. A type of faults can be figured out by analyzing a degree or shape of spreading of the CRs in ICRM. Moreover, ICRM is robust to variable operating conditions in practical settings because the scaled CRs that the effects of the operating conditions are reduced can highlight fault-induced irregularities. To demonstrate the effectiveness of ICRM, it was experimentally validated using a surface mounted PMSM, operated under variable-speed and different load torque conditions.

Simulation Analysis for Induction Motor Drive Fault Detection and Localization Under Variable Load and Speed Operation

The induction motors are widely used in the industry for their advantage of being reliable, rugged, and simple in construction. Nonetheless, the use of inverter-based drives has increased the overall system failure possibility mainly because of the power electronics switches used in the inverter itself. For that reason, it is crucial to monitor the inverter switches health during operation to avoid total system breakdown. A method to do so is by using the current vector trajectory analysis on the induction motor stator current signal. However, one disadvantage of using this method is that it is negatively affected by the load and speed variations. This paper is intended to discuss further on the matter and look into the methods that can be used to minimize the effects associated with the load and speed variations using computer simulation analysis.

Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions

Rotating machinery signals are usually dominated by rotating frequency harmonics, and the presence of these frequency components or change in their magnitudes indicate health condition. Order spectrum is widely applied in rotating machinery fault feature extraction, because of its capability in intuitive spectral representation of rotating frequency harmonics in order domain. However, some speed non-synchronous components are often present. They show wide-band features in order spectra, and hinder accurate identification of rotating frequency harmonic orders. To address this issue, a scheme is proposed to identify and remove speed non-synchronous components of either constant or time-varying frequency. To this end, surrogate test is generalized through new candidate construction method and nonstationarity based indicator for test criterion, to identify true nonstationary signal components adaptively and eliminate subjective influences. Vold-Kalman filter is used to separate signal components by exploiting its capability in mono-component decomposition of complex nonstationary signals. The proposed method is validated through analyses of both induction motor stator current signals and hydraulic turbine rotor vibration signal. The results demonstrate its advantages over conventional computed order spectrum analysis.

Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions

Vibration-based planetary gearbox fault diagnosis under time-varying speed conditions is challenging. This work exploits the advantages of motor stator current signal in easier accessibility and simpler frequency modulation structure, and applies the motor current signature analysis technique on planetary gearbox fault diagnosis. Firstly, the induction motor stator current signal with both planetary gearbox fault and airgap eccentricity under time-varying speed conditions is analytically modeled, and properties of time-varying planetary gearbox fault signatures are summarized. Then, to address the difficulties of revealing and presenting relatively weak and time-varying fault signatures in motor stator current signal, the adaptive iterative generalized demodulation is utilized. The Fourier transform surrogate test is integrated with iterative generalized demodulation algorithm to solve the problem of weak fault signature extraction, and Hilbert spectra of validated mono-components finally compose the time-frequency representation of analyzed motor stator current signal. Both numerical simulation as well as lab experimental evaluations in different gear fault cases, have been conducted to verify the correctness of the derived time-varying gear fault signatures, and the effectiveness of accurate planetary gearbox fault diagnosis.

A hybrid feature extraction methodology for gear pitting fault detection using motor stator current signal

A hybrid feature extraction methodology for gear pitting fault detection using motor stator current signal

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

Energy-Efficient of Non-Invasive Motor for On-Line Monitoring System Based on Reasoning Model

This paper mainly introduces the development process of energy-efficient of non-invasive motor for on-line monitoring system based on reasoning model. In addition, the function , composition and commissioning of shis system is introduced. In this system, in order to collect and analyze status recording of the motor running, operating parameters of motor speed, torque and power factor, efficiency are estimated on-line by using motor stator current signal feature analysis technology. In the paper, air-gap torque method is used to non-invasive electromotor for efficiency computation. The designing process of software for On-line monitoring system is shown as: Firstly, monitor motor’s operating parameters on-line, then, motor energy management is supported by the analysis of monitoring recording. At the same time, construct communication network for motor monitoring by using wireless communication technology about efficiency curve of industry motor. This system has the advantages of monitoring precision, easy installation, low costs, especially suitable for monitoring and energy management of efficiency for small and medium-sized motor, and it has been used widely in industrial production.