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Sensors and Sensing Systems for Condition Monitoring

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 28648

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Prof. Dr. Ada Fort

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Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy
Interests: chemical sensors; electronic sensors; electronic measurements
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tommaso Addabbo

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Guest Editor

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy
Interests: mixed-signal electronics; front-end electronics; nonlinear circuits and systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition Monitoring has made considerable progress in recent years, as a major component of machinery predictive maintenance in industry and transportation. Based on signals and data derived from sensors and sensing systems, Condition Monitoring technologies provide information regarding the status and future evolution of health conditions of machinery and plants, which is of vital importance for guaranteeing the efficiency and reliability of engineering assets.

The design of sensors and sensing systems for Condition Monitoring addresses multidisciplinary complex problems pertaining to, e.g., sensing materials, measurement techniques, electronics for hazardous environments, linear and nonlinear signal processing, data fusion, data mining, energy harvesting, sensor networks.

The aim of this Special Issue is to attract high-quality, innovative, and original works related but not restricted to the above mentioned research fields, including the following topics, relevant to Condition Monitoring:

Condition Monitoring methods, technologies, and systems;
Sensors, sensing materials and sensing systems;
Electronic measurement systems for hazardous environments;
Front-end electronics and signal conditioning;
Signal processing techniques;
Model and data driven prognostics;
Artificial intelligence, machine learning, feature extraction, and pattern recognition;
Data fusion and data mining;
Energy harvesting;
Sensor networks;
Intelligent devices and components

Prof. Dr. Ada Fort
Prof. Dr. Tommaso Addabbo
Guest Editors

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Published Papers (11 papers)

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Research

13 pages, 1784 KiB

Open AccessArticle

Real-Time Condition Monitoring System for Electrode Alignment in Resistance Welding Electrodes

by Daniel Ibáñez, Eduardo García, Jesús Soret and Julio Martos

Sensors 2022, 22(21), 8412; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218412 - 01 Nov 2022

Cited by 2 | Viewed by 2282

Abstract

Electrode misalignment, produced by mechanical fatigue or bad adjustments of the welding gun, leads to an increase in expulsions, deformations and quality problems of the welding joints. Different studies have focused on evaluations of the influence of a misalignment of the electrodes and the final quality of the weld nugget. However, few studies have focused on determining a misalignment of the electrodes to avoid problems caused by this defect, especially in industrial environments. In this paper, a method for performing the condition monitoring of electrode alignment degradation was developed following previous research, which has shown the relationship between the misalignment of short-circuited electrodes and the magnetic field generated by them. This method was carried out by means of a device capable of measuring the magnetic field. Finally, an integral system for the detection of misalignments in real production lines is presented. This system set behavior thresholds based on the experimentation, allowing the condition monitoring of the alignment after each welding cycle. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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20 pages, 12525 KiB

Open AccessArticle

Development of a Current Injection—Type Impedance Measurement System for Monitoring Soil Water Content and Ion Concentration

by Ryuki Shigemasu, Yuki Teraoka, Satoshi Ota, Harutoyo Hirano, Keita Yasutomi, Shoji Kawahito and Masato Futagawa

Sensors 2022, 22(9), 3509; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093509 - 05 May 2022

Cited by 3 | Viewed by 2034

Abstract

This study was conducted with the aim of developing a circuit system that enables the measurement of the moisture content and ion concentration with a simple circuit configuration. Our previous studies have shown that soil can be represented by an equivalent circuit of a parallel circuit of resistors and capacitors. We designed a circuit that can convert the voltage transient characteristics of the soil when a current is applied to it into a square wave and output frequency information and developed an algorithm to analyze the two types of square waves and calculate R and C. Normal operation was confirmed in the range of 10 kΩ–1 MΩ for the designed circuit, and the calculation algorithm matched within a maximum error of 5%, thus confirming the validity of the program. These successfully confirmed the changes in the water content and ionic concentration. The soil moisture content measurement succeeded in measuring a maximum error of about 10%, except at one point, and the soil ion concentration measurement succeeded in measuring a maximum error of 6.6%. A new, simple, noise-resistant moisture content and ion concentration measurement circuit system with square wave output has been realized. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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21 pages, 3735 KiB

Open AccessArticle

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset

by Elsie Fezeka Swana, Wesley Doorsamy and Pitshou Bokoro

Sensors 2022, 22(9), 3246; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093246 - 23 Apr 2022

Cited by 45 | Viewed by 5018

Abstract

Data-driven methods have prominently featured in the progressive research and development of modern condition monitoring systems for electrical machines. These methods have the advantage of simplicity when it comes to the implementation of effective fault detection and diagnostic systems. Despite their many advantages, the practical implementation of data-driven approaches still faces challenges such as data imbalance. The lack of sufficient and reliable labeled fault data from machines in the field often poses a challenge in developing accurate supervised learning-based condition monitoring systems. This research investigates the use of a Naïve Bayes classifier, support vector machine, and k-nearest neighbors together with synthetic minority oversampling technique, Tomek link, and the combination of these two resampling techniques for fault classification with simulation and experimental imbalanced data. A comparative analysis of these techniques is conducted for different imbalanced data cases to determine the suitability thereof for condition monitoring on a wound-rotor induction generator. The precision, recall, and f1-score matrices are applied for performance evaluation. The results indicate that the technique combining the synthetic minority oversampling technique with the Tomek link provides the best performance across all tested classifiers. The k-nearest neighbors, together with this combination resampling technique yielded the most accurate classification results. This research is of interest to researchers and practitioners working in the area of condition monitoring in electrical machines, and the findings and presented approach of the comparative analysis will assist with the selection of the most suitable technique for handling imbalanced fault data. This is especially important in the practice of condition monitoring on electrical rotating machines, where fault data are very limited. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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21 pages, 3727 KiB

Open AccessArticle

On Mechanical and Electrical Coupling Determination at Piezoelectric Harvester by Customized Algorithm Modeling and Measurable Properties

by Irene Perez-Alfaro, Daniel Gil-Hernandez, Nieves Murillo and Carlos Bernal

Sensors 2022, 22(8), 3080; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083080 - 17 Apr 2022

Cited by 2 | Viewed by 2551

Abstract

Piezoelectric harvesters use the actuation potential of the piezoelectric material to transform mechanical and vibrational energies into electrical power, scavenging energy from their environment. Few research has been focused on the development and understanding of the piezoelectric harvesters from the material themselves and the real piezoelectric and mechanical properties of the harvester. In the present work, the authors propose a behavior real model based on the experimentally measured electromechanical parameters of a homemade PZT bimorph harvester with the aim to predict its V_rms output. To adjust the harvester behavior, an iterative customized algorithm has been developed in order to adapt the electromechanical coupling coefficient, finding the relationship between the harvester actuator and generator behavior. It has been demonstrated that the harvester adapts its elongation and its piezoelectric coefficients combining the effect of the applied mechanical strain and the electrical behavior as a more realistic behavior due to the electromechanical nature of the material. The complex rms voltage output of the homemade bimorph harvester in the frequency domain has been successfully reproduced by the proposed model. The Behavior Real Model, BRM, developed could become a powerful tool for the design and manufacturing of a piezoelectric harvester based on its customized dimensions, configuration, and the piezoelectric properties of the smart materials. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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11 pages, 11660 KiB

Open AccessCommunication

Fatigue Measurements in an Existing Highway Concrete Bridge

by Harald Schuler and Martin Müller

Sensors 2022, 22(8), 2868; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082868 - 08 Apr 2022

Cited by 2 | Viewed by 1373

Abstract

Knowing the actual fatigue effects helps to assess the structural safety of bridges more accurately. Especially when the actions change or increase, it is important to be able to determine this. This paper deals with the measurement and recording of actual fatigue loads at a critical point of an existing bridge. It shows how long-term effects can be separated from short-term effects and how amplitudes from passing trucks can be counted. The paper also highlights the challenges that arise when measuring actual fatigue loads. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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11 pages, 3662 KiB

Open AccessArticle

Research on High Sensitivity Oil Debris Detection Sensor Using High Magnetic Permeability Material and Coil Mutual Inductance

by Chengjie Wang, Chenzhao Bai, Zhaoxu Yang, Hongpeng Zhang, Wei Li, Xiaotian Wang, Yiwen Zheng, Lebile Ilerioluwa and Yuqing Sun

Sensors 2022, 22(5), 1833; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051833 - 25 Feb 2022

Cited by 17 | Viewed by 1994

Abstract

Metallic contaminants (solid) are generated by friction pair, causing wear of equipment by enters the lubricating system. This poses a great potential threat to the normal operation of such machines. The timely analysis and detection of debris can lead to the avoidance of mechanical failures. Abnormal wear in machinery may produce debris exceeding 10 μm. The traditional inductance detection method has low sensitivity and cannot meet the actual detection requirements. To boost the sensitivity of the inductance sensor, the mutual inductance of coils and the strong magnetic conductivity of permalloy was utilized to design a high sensitivity inductance sensor for the detection of debris in lubricating oil. This design was able to detect 10–15 μm iron particles and 65–70 μm copper particles in the oil. The experimental results illustrate that low-frequency excitation is the best for detecting ferromagnetic particles, while high-frequency excitation has the best effect for detecting non-ferromagnetic particles. This paper demonstrates the significant advantages of coil mutual inductance, and strong magnetic conductivity of permalloy in improving the detection sensitivity of oil debris sensors. This will provide technical support for wear detection in mechanical equipment and fault diagnosis. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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14 pages, 5758 KiB

Open AccessArticle

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

by Bart Scheeren, Miroslaw Lech Kaminski and Lotfollah Pahlavan

Sensors 2022, 22(4), 1500; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041500 - 16 Feb 2022

Cited by 9 | Viewed by 1908

Abstract

In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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17 pages, 8879 KiB

Open AccessArticle

Harmonic Components Analysis of Emitted Ultraviolet Signals of Aged Transmission Line Insulators under Different Surface Discharge Intensities

by Saiful Mohammad Iezham Suhaimi, Nor Asiah Muhamad, Nouruddeen Bashir, Mohamad Kamarol Mohd Jamil and Mohd Nazri Abdul Rahman

Sensors 2022, 22(3), 722; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030722 - 18 Jan 2022

Cited by 4 | Viewed by 1492

Abstract

Flashover on transmission line insulators is one of the major causes of line outages due to contamination from the environment or ageing. Power utility companies practicing predictive maintenance are currently exploring novel non-contact methods to monitor insulator surface discharge activities to prevent flashover. This paper presents an investigation on the UV pulse signals detected using UV pulse sensor due to the discharges on the insulator surfaces under varying contamination levels and insulator ages. Unaged and naturally aged insulators (0 to >20 years) were artificially contaminated (none, light to heavy contamination). The electrical stresses on the insulator surfaces were varied to generate varying discharge intensity levels on the surfaces of the insulator. The DC and harmonic components of UV pulse signals detected during surface discharges were recorded and analysed. Results show a positive correlation between the discharge intensity level of contaminated and aged transmission insulators with the DC and harmonic components of the UV pulse signals. Furthermore, the study revealed that under dry insulator surface conditions, insulator ageing has a more profound effect during discharges than contamination level. The findings from this study suggest that the use of UV pulse sensors to monitor UV pulse signals emitted during insulator surface discharges can be another novel non-contact method of monitoring transmission line insulator surface conditions. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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14 pages, 4352 KiB

Open AccessArticle

Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder

by Imene Mitiche, Tony McGrail, Philip Boreham, Alan Nesbitt and Gordon Morison

Sensors 2021, 21(21), 7426; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217426 - 08 Nov 2021

Cited by 8 | Viewed by 2665

Abstract

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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20 pages, 7251 KiB

Open AccessArticle

Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection

by Chao-Ching Ho, Wei-Chi Chou and Eugene Su

Sensors 2021, 21(21), 7074; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217074 - 25 Oct 2021

Cited by 9 | Viewed by 3348

Abstract

This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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23 pages, 26088 KiB

Open AccessArticle

Ion Current Sensor for Gas Turbine Condition Dynamical Monitoring: Modeling and Characterization

by Tommaso Addabbo, Ada Fort, Elia Landi, Marco Mugnaini, Lorenzo Parri, Valerio Vignoli, Alessandro Zucca and Christian Romano

Sensors 2021, 21(20), 6944; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206944 - 19 Oct 2021

Cited by 2 | Viewed by 2213

Abstract

This paper aims to thoroughly investigate the potential of ion current measurements in the context of combustion process monitoring in gas turbines. The study is targeted at characterizing the dynamic behavior of a typical ion-current measurement system based on a spark-plug. Starting from the preliminary study published in a previous work, the authors propose a refined model of the electrode (spark plug), based on the Langmuir probe theory, that incorporates the physical surface effects and proposes an optimized design of the conditioning electronics, which exploits a low frequency AC square wave biasing of the electrodes and allows for compensating some relevant parasitic effects. The authors present experimental results obtained in the laboratory, which allow for the evaluation of the validity of the model and the interpreting of the characteristics of the measurement signal. Finally, measurements carried out in the field on an industrial combustor are presented. The results confirm that the charged chemical species density sensed by the proposed measurement system and related to the mean value of the output signal is an indicator of the ‘average’ combustion process conditions in terms e.g., of air/fuel ratio, whereas the high frequency spectral component of the measured signal can give information related to the turbulent regime and to the presence of pressure pulsations. Results obtained with a prototype system demonstrated an achievable resolution of about 5 Pa on the estimated amplitude, even under small biasing voltage (22.5 V) and an estimated bandwidth of 10 kHz. Full article

(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)

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