Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 14708

Special Issue Editor


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Guest Editor
Department of Management, Information and Production Engineering, University of Bergamo, Via Galvani 5, 24044 Dalmine, BG, Italy
Interests: system identification; fault diagnosis

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your papers to our Special Issue on Electro-Mechanical Actuator, Diagnostic and Fault-Tolerant Control Systems.

Electrically-powered actuation systems do not suffer from many of the inherent shortcomings of hydraulic, pneumatic, and mechanical ones; they are relatively flexible and light, more environmentally sustainable, and have higher efficiency. To further improve their reliability and efficiency, electromechanical actuators (EMA) can be designed with fault-tolerant architectures and equipped with diagnostic and fault-tolerant algorithms. Since modern systems’ technology is characterized by the interconnection of many automated components, the detection and accommodation of a faulty component is essential to avoid the propagation of the fault to the whole system. In this context, the recent concept of health-aware control is rapidly emerging as a way to incorporate system health information into control actions.

In summary, the aim of this Special Issue is to foster and promote research on diagnosis and fault-tolerant control of electromechanical actuators, both methodological and experimental, with no restrictions in terms of the applicative domain. The topics of interest to the Special Issue include, but are not limited to:

  • Innovative EMA fault-tolerant design;
  • Fault diagnosis, condition monitoring and prognostics of EMA;
  • Fault-tolerant control of EMA;
  • Health-aware control of EMA;
  • Applications of electromechanical actuators.

Dr. Mirko Mazzoleni
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electromechanical actuators
  • fault diagnosis
  • condition monitoring
  • predictive maintenance
  • fault-tolerant control
  • health-aware control

Published Papers (8 papers)

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Research

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19 pages, 3685 KiB  
Article
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 1204
Abstract
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
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19 pages, 9386 KiB  
Article
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 1230
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
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26 pages, 7727 KiB  
Article
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 1259
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
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15 pages, 6981 KiB  
Article
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 1397
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
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17 pages, 9493 KiB  
Article
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 1127
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
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19 pages, 5426 KiB  
Article
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 1445
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
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22 pages, 8800 KiB  
Article
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 1326
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
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Review

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21 pages, 1712 KiB  
Review
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 4774
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
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