Inter-turn Fault Diagnosis Research Articles

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

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.

Interacting Multiple Model Framework for Incipient Diagnosis of Interturn Faults in Induction Motors

Interacting Multiple Model Framework for Incipient Diagnosis of Interturn Faults in Induction Motors

A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.

Identification method for inter-turn faults in transformers based on digital twin concept

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization—ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers.

Diagnosis of inter-turn short circuit fault in IPMSMs based on the combined use of greedytracking and random forest

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm. The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

Diagnosis and location of inter-turn short circuit fault in pumped storage unit

In order to monitor the inter-turn insulation health of the rotor windings of pumped storage unit (PSU) in real time, a fault diagnosis method based on magnetic field detection is proposed in this study. Firstly, the characteristic harmonics of the air-gap flux density (AGFD) corresponding to the different states are obtained by deriving the expressions for the AGFD in the normal and inter-turn short circuit (ITSC) states of the rotor winding. Secondly, the detection principle of Hall sensor and its application are studied, and an intelligent detection theory combining frequency-resolved spectrum (FRS) and radar chart is proposed. Thirdly, constructing a electromagnetic model based on the actual operating PSU size, then the time/frequency characteristics of the Hall induced electromotive force (HIEF) are analysed, from which the intelligent diagnostic spectrums are constructed for fault detection. Finally, the Hall probe detection sensitivity is calibrated to 1 mV/mT. By installing Hall probe on the slot wedge surface of the generator stator, HIEF signals for different ITSC levels are acquired and integrated with pole positioning signals for condition monitoring, which can effectively identify and locate ITSC fault, while the detection sensor has significant advantages in terms of size and installation simplicity.

Inter-Turn Fault Diagnosis and Control for Five-Phase PMSMs by Disturbance Observer

Inter-Turn Fault Diagnosis and Control for Five-Phase PMSMs by Disturbance Observer

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

Diagnosis of Inter-Turn Short Circuit Fault in BLDCM Based on Coupled High-Frequency Signal Injection

An MPC-Based Online Inter-Turn Fault Diagnosis Method for Induction Motors with Fault Localization

An MPC-Based Online Inter-Turn Fault Diagnosis Method for Induction Motors with Fault Localization

Stator Turn Fault Diagnosis and Severity Assessment in Converter-Fed Induction Motor Using Flat Diagnosis Structure Based on Deep Learning Approach

This work aims to explore the hybrid deep learning techniques for early interturn fault diagnosis and fault severity assessment in variable frequency induction motor drive. However, the existing literature mostly focuses on identifying the fault patterns, whereas the assessment of fault deterioration before it reaches the extreme degradation state has been less investigated. In this paper, a hybrid structure combining two-dimensional convolution neural network (2DCNN) and long-short term memory/gated recurrent unit (LSTM/GRU) has been modelled as a relatively lightweight flat diagnosis structure (FDS) for early fault identification and its severity assessment simultaneously in converter-fed induction motor. Initially, the hierarchical layers of 2DCNN extract the effective features automatically for fault detection and its severity identification simultaneously. Then, these features are presented as suitable inputs to LSTM/GRU for classification. A comparative evaluation of the proposed hybrid method with the standalone structure, namely 2DCNN, LSTM and SVM proves the advantages of the proposed scheme, and its applicability for both fault diagnosis and fault severity assessment in industrial drive systems, under variable frequencies and load variations.

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor

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.

CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection

Condition monitoring, fault diagnosis, and scheduled maintenance of wind turbines (WTs) are becoming a necessity to maximize their economic benefits and reduce their downtime. One of the critical faults in WTs is inter-turn short-circuit faults (ITSCF) in their rotating machines. Being dependent on higher wind speeds, offshore wind farms are more efficient than onshore ones, yet at the cost of higher ITSCF, more maintenance requirements, and difficulty in accessibility. Thus, they require an efficient non-contact fault diagnosis technique to enable operators to inspect all WT components with minimal contact. Among existing fault detection technologies, Infrared thermography (IRT) is considered as a non-invasive and non-destructive anomaly detection technique based on capturing thermal images by IR cameras, making it suitable for offshore harsh environments. Meanwhile, deep learning (DL) fault diagnosis approaches have proven to be promising intelligent tools with minimum human labor. This paper proposes an efficient and robust ensemble DL-based diagnosis method for ITSCF detection in induction rotating machines, besides identifying fault locations and short-circuit severity. The proposed technique is quite convenient for early diagnosis of offshore WT generator faults since it depends on IRT technology. Compared to previous studies, the proposed approach outperforms its counterparts since it integrates eight DL models, rather than using a single model, thus merging all their structural benefits altogether. Moreover, a cascaded feature fusion and selection procedure is introduced. First, the deep features extracted from the DL models are fused, then the most influential features are selected using a hybrid feature selection scheme. Thus, the classification accuracy is enhanced to reach 100% and meanwhile, the input features’ size to the classifier is reduced, decreasing classification complexity and training time. Finally, no clustering or segmentation phases are required in the proposed technique, resulting in a further decrease in the diagnosis time and computation burden.

Improvement in Inter-Turn Faults Detection in Poles of Synchronous Machines by FRA Grounding Connection

Inter-turn faults are among the most common failures in electrical machines. While Frequency Response Analysis, (FRA) has been extensively used for power transformers diagnosis for the last 40 years. In this conventional, accurate and non-destructive technique for power transformer diagnosis, it is a usual practice to connect the chassis of the FRA test device to the transformer housing, normally grounded. The same practice has been applied for pole diagnosis, although no procedure has yet been established as standard since pole diagnosis using FRA is still an experimental technique. Nevertheless, this paper shows a different approach to diagnose inter-turns faults in poles by isolating the FRA chassis from the iron core. In this way, the capacity to ground has less influence, increasing FRA test sensitivity for inter-turn fault diagnosis. The method has been verified by experimental results in several spare poles belonging to 106 MVA and 40 MVA synchronous hydro-generators and also by two simulation models with both types of connections, grounded and FRA-isolated, achieving positive results.

Early Stator Fault Detection and Condition Identification in Induction Motor Using Novel Deep Network

Fault diagnosis of induction motor is an important task for both the researchers and industry. The interturn fault is one of the frequented faults and accounts for more than 37% of the failures. Thus, the diagnosis of interturn fault at an early stage has become vital to avoid the catastrophic failures and production losses. In this work, the advantages of deep learning-based methods are explored to detect the interturn fault at an incipient stage. Hybrid architectures has been proposed in this work for incipient inter turn fault diagnosis [i.e., 1D convolution neural network-long short-term memory (1DCNN-LSTM) and 1DCNN-gated recurrent unit (GRU) based methods]. The performance of the hybrid methods has been compared with standalone techniques (1DCNN, LSTM, and GRU), and the results show that the hybrid models (1DCNN-LSTM and 1DCNN-GRU) outperform the individual architectures (in terms of accuracy, sensitivity, and specificity), for fault diagnosis and isolation. The computational time has also been found to be comparable, which is an added advantage for fast diagnosis tasks. The approach combines the feature extraction and classification tasks, to perform early diagnosis, in presence of ambiguous conditions. Here, the incipient diagnosis of fault has been augmented with condition identification i.e., healthy (balanced voltages), healthy (unbalanced voltages), faulty (balanced voltages), and faulty (unbalanced voltages), to effect both early detection and isolation. The performance metrics show the robustness of the proposed method for not only detecting fault, but also identifying such confusing conditions for critical applications, in which induction motors are employed.

A Voltage-Distortion-Based Method for Robust Detection and Location of Interturn Fault in Permanent Magnet Synchronous Machine

In this article, a voltage-distortion-based method is presented for robust detection and location of interturn fault (ITF) in permanent magnet synchronous machine (PMSM). The mathematical models of PMSM under healthy and fault conditions are presented. According to the mathematical model, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis voltage distortions owing to ITF are estimated through Luenberger observer. On the basis of the voltage distortions, a fault characteristic signal (FCS) is constructed for ITF diagnosis. Through analyzing FCS, the second harmonic component in FCS is chosen to construct the fault indicators for fault diagnosis. The simulation and experimental results show that the ITF diagnosis is effectively realized through the presented method. Additionally, the presented method requires no additional hardware and has good robustness against parameter mismatch and rotating speed variation.

Novel Approach to Fault-Tolerant Control of Inter-Turn Short Circuits in Permanent Magnet Synchronous Motors for UAV Propellers

This paper deals with the development of a novel fault-tolerant control technique aiming at the diagnosis and accommodation of inter-turn short circuit faults in permanent magnet synchronous motors for lightweight UAV propulsion. The reference motor is driven by a four-leg converter, which can be reconfigured in case of a phase fault by enabling the control of the central point of the motor Y-connection. A crucial design point entails the development of fault detection and isolation (FDI) algorithms capable of minimizing the failure transients and avoiding the short circuit extension. The proposed fault-tolerant control is composed of two sections: the first one applies a novel FDI algorithm for short circuit faults based on the trajectory tracking of the motor current phasor in the Clarke plane; the second one implements the fault accommodation, by applying a reference frame transformation technique to the post-fault commands. The control effectiveness is assessed via nonlinear simulations by characterizing the FDI latency and the post-fault performances. The proposed technique demonstrates excellent potentialities: the FDI algorithm simultaneously detects and isolates the considered faults, even with very limited extensions, during both stationary and unsteady operating conditions. In addition, the proposed accommodation technique is very effective in minimizing the post-fault torque ripples.

Integration of Interturn Fault Diagnosis and Fault-Tolerant Control for PMSM Drive System

This article first studies the integration of interturn fault diagnosis and fault-tolerant (FT) control of permanent magnet synchronous machine (PMSM) drive system. In this article, interturn fault is diagnosed by zero-sequence voltage component and the torque ripple due to interturn fault is reduced by the current injection-based FT control strategy. The main novelty of this article is that the injection currents for FT control are directly calculated based on the already obtained information during the fault diagnosis process, thus reducing the complexity of FT control and making interturn fault diagnosis and FT control convenient integration. Both the simulation and experimental results validate the effectiveness of the proposed integration method for the PMSM drive system.

Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model

The importance of induction motor (IM) drives in industrial applications, especially in the renewable energy sector is unarguable due to their fast-dynamic response. Their monitoring is key to avoid downtime and economic losses. Even though a lot of research has been focused on incipient fault diagnosis in induction machines, early diagnosis in power electronic drive-fed machines is still a challenge. In this article, a novel two-level hybrid hierarchical convolution neural network with support vector machine (HCNN-SVM) is proposed for incipient interturn fault diagnosis of drive-fed machines. The first level of the proposed structure is intended to recognize the pattern of interturn fault in drive-fed IM, while the second level is developed to identify the fault severity. First, the effective features are extracted automatically using the shared layers of HCNN for fault diagnosis and fault severity evaluation at the same time. The features obtained using the HCNN are suitably used to train SVM for classification. Comparison of the results with the existing architectures, namely, HCNN and SVM, show the effectiveness of proposed hybrid method. The experimental results show that the hybrid HCNN-SVM is fast and highly accurate in identifying the interturn fault pattern and evaluating its severity.