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Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines
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Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 22612

Special Issue Editors

Prof. Dr. Martin Riera-Guasp

E-Mail Website
Guest Editor
Institute for Energy Engineering, Universitat Politècnica de València, Valencia, Spain
Interests: condition monitoring of electrical machines; applications of signal analysis techniques to electrical engineering and efficiency in electric power applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Manuel Pineda-Sanchez

E-Mail Website
Guest Editor
Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: fault diagnosis of electrical machines; reduced order-modeling of electromagnetic devices; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Javier Martinez-Roman

E-Mail Website
Guest Editor
Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: fault diagnosis of electrical machines; control of electrical machines and drives
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ruben Puche-Panadero

E-Mail Website
Guest Editor
Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: induction motor fault diagnosis; numerical modeling of electrical machines; advanced automation processes and electrical installations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Angel Sapena-Bano

E-Mail Website
Guest Editor
Institute for Energy Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: induction motor fault diagnosis; numerical modeling of electrical machines; advanced automation processes and electrical installations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical machines are the key components of many industrial processes, as well as in everyday life. They may provide mechanical power (e.g., induction motors, permanent magnet motors) or electrical power (e.g., synchronous generators, wind turbines), and their vital role has increased along with the growing widespread use of electrical vehicles, renewable energies, robots, drones, etc. A growing trend is the integration of electrical machines in information systems aimed at tracking production data, optimizing their functional setup, or assessing the condition of the machine in order to prevent or minimize the impact of sudden failures. For this integration to succeed, it is necessary to analyze diverse sensor parameters (currents, vibrations, axial fluxes, speed, etc.) using signal processing techniques, and to present this information to the end user while taking into account the different information channels available in modern communication systems (specialized SCADA systems, web pages, mobile apps, cloud repositories, etc.).

Therefore, in recent years, the fault diagnosis and monitoring technologies of electrical machines have attracted increasing attention from both academia and industry. Both high volumes and high quality of information are being demand from multiple types of sensor data, but sensors are also subject to failure, which must be accounted for in the diagnostic systems. The integration of distributed sensor networks in model-based, signal-based, knowledge-based, and hybrid/active diagnostic systems is a challenging issue which requires expertise from a broad set of disciplines, such as artificial intelligence, adaptive observer design, statistical estimation, data dimension reduction techniques, etc. On the other side, the acquired information can be stored, processed, and delivered using modern cloud-based software services and big-data technologies.

We invite researchers from both academia and industry to submit original and unpublished manuscripts to this Special Issue to showcase some of the recent developments within these topics.

The goal of the Special Issue is to publish the most recent research results and industrial applications of sensors in fault diagnosis and monitoring of electrical machines. Topics that are suitable for this Special Issue include, but are not limited to: 

  • Data-driven and model-based sensor fault diagnosis;
  • Integration of high-volume sensor data in the design of applications for fault diagnosis of electrical machines and drives;
  • Sensors in advanced electrical machines—fault diagnosis and monitoring applications in different industrial sectors;
  • Methods, concepts, and performance assessment for improving the fault diagnosis of existing techniques in the field of electrical machines;
  • Electrical drives as sensors in industrial processes;
  • Cloud-based software services for fault diagnosis and monitoring of electrical machines.

Prof. Dr. Martin Riera-Guasp
Prof. Dr. Manuel Pineda-Sanchez
Prof. Dr. Javier Martinez-Roman
Prof. Dr. Ruben Puche-Panadero
Prof. Dr. Angel Sapena-Bano
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • distributed sensor networks
  • data-driven fault diagnosis systems for electrical machines and drives
  • knowledge-based fault diagnosis control systems
  • electrical machines and drives as sensors for fault diagnosis

Published Papers (4 papers)

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Research

23 pages, 6702 KiB  
Article
Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems
by Francisco Arellano-Espitia, Miguel Delgado-Prieto, Victor Martinez-Viol, Juan Jose Saucedo-Dorantes and Roque Alfredo Osornio-Rios
Sensors 2020, 20(14), 3949; https://doi.org/10.3390/s20143949 - 16 Jul 2020
Cited by 29 | Viewed by 3624
Abstract
Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be [...] Read more.
Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis. Full article
(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines)
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20 pages, 9999 KiB  
Article
Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors
by Martin Valtierra-Rodriguez, Jesus R. Rivera-Guillen, Jesus A. Basurto-Hurtado, J. Jesus De-Santiago-Perez, David Granados-Lieberman and Juan P. Amezquita-Sanchez
Sensors 2020, 20(13), 3721; https://doi.org/10.3390/s20133721 - 03 Jul 2020
Cited by 44 | Viewed by 4930
Abstract
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In [...] Read more.
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases. Full article
(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines)
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19 pages, 3749 KiB  
Article
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
by Rafia Nishat Toma, Alexander E. Prosvirin and Jong-Myon Kim
Sensors 2020, 20(7), 1884; https://doi.org/10.3390/s20071884 - 28 Mar 2020
Cited by 143 | Viewed by 10694
Abstract
Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich [...] Read more.
Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults. Full article
(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines)
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23 pages, 4954 KiB  
Article
An Approximate Estimation Approach of Fault Size for Spalled Ball Bearing in Induction Motor by Tracking Multiple Vibration Frequencies in Current
by Chidong Qiu, Xinbo Wu, Changqing Xu, Xiang Qiu and Zhengyu Xue
Sensors 2020, 20(6), 1631; https://doi.org/10.3390/s20061631 - 14 Mar 2020
Cited by 17 | Viewed by 2339
Abstract
Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In [...] Read more.
Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In the present research, an approximate estimation approach based on current is introduced. The proposed approach is easy to implement for existing inverter-driven induction motors without complicated calculations and additional sensors, immune to external disturbances, and suitable for harsh conditions. Firstly, a feature transmission route from spall, to Hertzian forces, and then to friction torque is simulated based on a spall model and dynamic model of the bearing. Based on simulated results, the relation between spall size and the multiple characteristic vibration frequencies in friction torque is revealed. Secondly, the multiple characteristic vibration frequencies modulated in the current is investigated. Analysis results show that those frequencies modulated in the current are independent of each other, without spectrum overlap. Thirdly, to address the issue of which fault features modulated in the current are very weak, a fault-feature-highlighting approach based on reduced voltage frequency ratio is proposed. Finally, experimental tests were conducted. The obtained results validate that the proposed approach is feasible and effective for spall size estimation. Full article
(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines)
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