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Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines II

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

Deadline for manuscript submissions: closed (10 January 2023) | Viewed by 7501

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Special Issue Editor

Prof. Dr. Martin Riera-Guasp

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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
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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
Guest Editor

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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, 1105 KiB

Open AccessArticle

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

by Nasir Ud Din, Li Zhang and Yatao Yang

Sensors 2023, 23(4), 1927; https://doi.org/10.3390/s23041927 - 08 Feb 2023

Cited by 6 | Viewed by 1999

Abstract

Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation. Full article

(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines II)

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16 pages, 915 KiB

Open AccessArticle

Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison

by Michele Sintoni, Elena Macrelli, Alberto Bellini and Claudio Bianchini

Sensors 2023, 23(2), 1046; https://doi.org/10.3390/s23021046 - 16 Jan 2023

Cited by 3 | Viewed by 1758

Abstract

In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost. Full article

(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines II)

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26 pages, 3841 KiB

Open AccessArticle

Monitoring Single-Phase LV Charging of Electric Vehicles

by Piotr Kuwałek and Grzegorz Wiczyński

Sensors 2023, 23(1), 141; https://doi.org/10.3390/s23010141 - 23 Dec 2022

Cited by 1 | Viewed by 1551

Abstract

The paper presents the results of the monitoring process of the charging of an electric vehicle battery pack. Battery pack charging with a capacity of 58

kWh

was monitored in a single-phase 230

V

/50

Hz

circuit. The slow charging system used was [...] Read more.

The paper presents the results of the monitoring process of the charging of an electric vehicle battery pack. Battery pack charging with a capacity of 58

kWh

was monitored in a single-phase 230

V

/50

Hz

circuit. The slow charging system used was configured to obtain a current of 10

A

. During monitoring, the focus was on the recognition of the charging, considering the impact of this process on power quality and, consequently, on the reliability of electrical machines. Research results show that the monitored charges are one-, two-, or three-stage processes. The variations in the currents, power, and higher harmonic contents were observed. The effects of such variations depend on the properties of the power grid at the point of connection of the charging system. Knowledge of the variation of the voltages, currents, and active and reactive power allows for the determination of the requirements of the measuring equipment used for charging the monitoring, including the selection of discrimination/averaging time of monitored quantities. The research results also indicate the need for continuous monitoring of the power quality in the power supply circuit of electrical loads, e.g., electrical machines. Continuous monitoring supports the diagnostics of electrical machines and allows the appropriate measures to increase their reliability. Full article

(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines II)

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16 pages, 5868 KiB

Open AccessArticle

Speed Estimation of Six-Phase Induction Motors, Using the Rotor Slot Harmonics

by Khaled Laadjal, Fernando Bento, Hugo R. P. Antunes, Mohamed Sahraoui and Antonio J. Marques Cardoso

Sensors 2022, 22(21), 8157; https://doi.org/10.3390/s22218157 - 25 Oct 2022

Cited by 3 | Viewed by 1579

Abstract

Multiphase machines have recently been promoted as a viable alternative to traditional three-phase machines. Most experts are looking for strategies to estimate the rotation speed of such complex systems, since speed data are required for high-performance control purposes. Traditionally, electromechanical sensors were used to detect the rotor speed of electric motors. These devices are extremely accurate, but they are also delicate and costly to deploy. New speed estimating algorithms must be created for these situations. This paper looks at how to estimate rotor speed in symmetrical six-phase induction motors (IMs) using a novel strategy for rotor speed estimation based on the Short Time Fourier Transform (STFT) method. The technique is based on tracking the frequencies of the rotor slot harmonics (RSH) seen in most squirrel-cage IM stator currents, thus assuring a broad range of applications. To monitor the RSH, the STFT employs a sliding window to perform the discrete Fourier transform technique, making it more suitable for online use with noisy and nonstationary signals. Experimental tests demonstrate the effectiveness of the suggested approach. Full article

(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines II)

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