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19 pages, 3685 KiB

Open AccessArticle

Transformer Fault Diagnosis Method Based on TimesNet and Informer

by Xin Zhang, Kaiyue Yang and Liaomo Zheng

Actuators 2024, 13(2), 74; https://doi.org/10.3390/act13020074 - 14 Feb 2024

Viewed by 1163

Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today’s engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by [...] Read more.

Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today’s engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by adding the MCA module to the Inception structure of the original TimesBlock to reduce the model complexity and computational burden; second, the MUSE attention mechanism is introduced into the original TimesNet to act as a bridge, so that associations can be carried out effectively among the local features, thus enhancing the modeling capability of the model; finally, when constructing the feature module, the TimesNet and Informer multilevel parallel feature extraction modules are introduced, making full use of the local features of the convolution and the global correlation of the attention mechanism module for feature summarization, so that the model learns more time-series information. To verify the effectiveness of the proposed method, the model is trained and tested on the public DGA dataset, and the model is compared and experimented with classical models such as Informer and Transformer. The experimental results show that the model has a strong learning ability for transformer fault data and has an advantage in accuracy compared with other models, which can provide a reference for transformer fault diagnosis. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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19 pages, 9386 KiB

Open AccessArticle

Diagnosis of Power Switch Faults in Three-Phase Permanent Magnet Synchronous Motors via Current-Signature Technique

by Aleksander Suti and Gianpietro Di Rito

Actuators 2024, 13(1), 25; https://doi.org/10.3390/act13010025 - 08 Jan 2024

Cited by 2 | Viewed by 1216

Abstract

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke [...] Read more.

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke plane through elliptical fittings, up to detecting and isolating the fault depending on the characteristics of the signature deviation from the nominal one. As a rough approximation, as typically proposed in the literature, the fault of one out of six power switches implies that, at constant speed operation, the phasor trajectory deviates from the nominal circular path up to a semi-circular “D-shape” signature, the inclination of which depends on the failed converter leg. However, this evolution can significantly deviate in practical cases, due to the dynamics related to the transition of motor phase connections from failed to active switches. The study demonstrates that an online ellipse fitting of the current signature can be effective for diagnosis, through correlating the ellipse centre to the location of the failed switch. The performances of the proposed monitoring technique are here assessed via the nonlinear simulation of a PMSM employed for the propulsion of a lightweight fixed-wing Unmanned Aerial Vehicle (UAV), by quantifying the fault latencies and the related transients. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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26 pages, 7727 KiB

Open AccessArticle

Bearing Fault Diagnosis Method Based on Adversarial Transfer Learning for Imbalanced Samples of Portal Crane Drive Motor

by Yongsheng Yang, Zhongtao He, Haiqing Yao, Yifei Wang, Junkai Feng and Yuzhen Wu

Actuators 2023, 12(12), 466; https://doi.org/10.3390/act12120466 - 15 Dec 2023

Viewed by 1250

Abstract

Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover, the problem of imbalanced fault samples has [...] Read more.

Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover, the problem of imbalanced fault samples has a more pronounced influence on the application of novel fault diagnosis methods. To address this, the paper presents a new method called bidirectional gated recurrent domain adversarial transfer learning (BRDATL), specifically designed for imbalanced samples from portal cranes’ drive motor bearings. Initially, a bidirectional gated recurrent unit (Bi-GRU) is used as a feature extractor within the network to comprehensively extract features from both source and target domains. Building on this, a new Correlation Maximum Mean Discrepancy (CAMMD) method, integrating both Correlation Alignment (CORAL) and Maximum Mean Discrepancy (MMD), is proposed to guide the feature generator in providing domain-invariant features. Considering the real-time data characteristics of portal crane drive motor bearings, we adjusted the CWRU and XJTU-SY bearing datasets and conducted comparative experiments. The experimental results show that the accuracy of the proposed method is up to 99.5%, which is obviously higher than other methods. The presented fault diagnosis model provides a practical and theoretical framework for diagnosing faults in portal cranes’ field operation environments. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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15 pages, 6981 KiB

Open AccessArticle

Design and Testing of Disconnection Actuators for Enhancing Safety and Preventing Failure Escalation

by Yusuf Akcay, Oliver Tweedy, Paolo Giangrande and Michael Galea

Actuators 2023, 12(11), 429; https://doi.org/10.3390/act12110429 - 20 Nov 2023

Viewed by 1389

Abstract

The growing demand for reliability has led to an increased interest in developing effective disconnection systems for enhancing the safety of and preventing failure escalation in engineering systems. Considering this prospect, the design optimization of two disconnection actuators composed of a coaxial magnetic [...] Read more.

The growing demand for reliability has led to an increased interest in developing effective disconnection systems for enhancing the safety of and preventing failure escalation in engineering systems. Considering this prospect, the design optimization of two disconnection actuators composed of a coaxial magnetic coupling linked to an electromagnetic device is presented and discussed. The disconnection actuator delivers a contactless torque transmission through the coaxial magnetic coupling, whereas the torque transfer is interrupted by the electromagnetic device in case a failure is detected via a dedicated algorithm. The performed design procedure relies on 2D finite element analysis, and trade-off studies are carried out to achieve an optimized geometry of an electromagnetic device. Finally, two disconnection actuators, for high-speed and high-torque applications, are prototyped and tested, with the aim of evaluating their disconnection capability. For both disconnection actuators, the developed force and voltage–current characteristics are measured along with the disconnection time. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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17 pages, 9493 KiB

Open AccessArticle

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

by Chao Zhang, Qixuan Huang, Ke Yang and Chaoyi Zhang

Actuators 2023, 12(4), 154; https://doi.org/10.3390/act12040154 - 02 Apr 2023

Cited by 1 | Viewed by 1116

Abstract

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always [...] Read more.

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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19 pages, 5426 KiB

Open AccessArticle

Rotor Faults Diagnosis in PMSMs Based on Branch Current Analysis and Machine Learning

by Yinquan Yu, Haixi Gao, Shaowei Zhou, Yue Pan, Kunpeng Zhang, Peng Liu, Hui Yang, Zhao Zhao and Daniel Makundwaneyi Madyira

Actuators 2023, 12(4), 145; https://doi.org/10.3390/act12040145 - 28 Mar 2023

Cited by 7 | Viewed by 1430

Abstract

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and hybrid fault of a permanent magnet synchronous motor (PMSM) with a slot pole ratio of 3/2 and several times of it, this paper proposes a [...] Read more.

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and hybrid fault of a permanent magnet synchronous motor (PMSM) with a slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and a machine learning algorithm. First, the analysis of the electrical signal of the PMSM after various types of rotor faults shows that there are differences in the time domain of the electrical signal of the PMSM after three types of rotor faults. Considering the symmetry of the structure of the PMSM with a slot-pole ratio of 3/2 and its integer multiples, the changes in the time domain of the phase currents cancel each other after the fault, and the time domain fluctuations of the stator branch currents that do not cancel each other are chosen as the characteristics of the fault classification in this paper. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, a genetic algorithm is used to optimize the parameters of a support vector machine (SVM), and the GA-SVM model is constructed as a classifier for multifault classification of permanent magnet synchronous motors to classify these three types of faults. The fault classification results show that the proposed method using branch current signals combined with GA-SVM can effectively diagnose faulty PMSM. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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22 pages, 8800 KiB

Open AccessArticle

Intelligent Fault Prognosis Method Based on Stacked Autoencoder and Continuous Deep Belief Network

by Chao Zhang, Yibin Zhang, Qixuan Huang and Yong Zhou

Actuators 2023, 12(3), 117; https://doi.org/10.3390/act12030117 - 09 Mar 2023

Cited by 1 | Viewed by 1316

Abstract

Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose is to predict the future working status of the machine based on the collected status information of the machine. However, on one hand, the model health indices based [...] Read more.

Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose is to predict the future working status of the machine based on the collected status information of the machine. However, on one hand, the model health indices based on the information collected by the sensors will directly affect the evaluation results of the system. On the other hand, because the model health index is a continuous time series, the effect of feature learning on continuous data also affects the results of fault prognosis. This paper makes full use of the autonomous information fusion capability of the stacked autoencoder and the strong feature learning capability of continuous deep belief networks for continuous data, and proposes a novel fault prognosis method. Firstly, a stacked autoencoder is used to construct the model health index through the feature learning and information fusion of the vibration signals collected by the sensors. To solve the local fluctuations in the health indices, the exponentially weighted moving average method is used to smooth the index data to reduce the impact of noise. Then, a continuous deep belief network is used to perform feature learning on the constructed health index to predict future performance changes in the model. Finally, a fault prognosis experiment based on bearing data was performed. The experimental results show that the method combines the advantages of stacked autoencoders and continuous deep belief networks, and has a lower prediction error than traditional intelligent fault prognosis methods. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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Review

Jump to: Research

21 pages, 1712 KiB

Open AccessReview

Fault Detection for Point Machines: A Review, Challenges, and Perspectives

by Xiaoxi Hu, Tao Tang, Lei Tan and Heng Zhang

Actuators 2023, 12(10), 391; https://doi.org/10.3390/act12100391 - 18 Oct 2023

Cited by 5 | Viewed by 4751

Abstract

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds [...] Read more.

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection. Full article

(This article belongs to the Special Issue Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems)

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