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33 pages, 1119 KiB

Open AccessArticle

Non-Invasive Parameter Identification of DC Arc Models for MV Circuit Breaker Diagnostics

by Gabriele D’Antona, Camilo Trujillo-Arboleda, Massimiliano Amato and Marco Riva

Sensors 2025, 25(10), 3161; https://doi.org/10.3390/s25103161 - 17 May 2025

Viewed by 104

Accurate electrical arc modeling with physically meaningful parameters is essential for the assessment of medium-voltage DC circuit breakers in industrial and railway applications. Laboratory testing and characterization, as outlined in the IEC 61992 standard series for railway applications, typically provide data to asses [...] Read more.

Accurate electrical arc modeling with physically meaningful parameters is essential for the assessment of medium-voltage DC circuit breakers in industrial and railway applications. Laboratory testing and characterization, as outlined in the IEC 61992 standard series for railway applications, typically provide data to asses the operational behavior of the componentsin the power distribution system, including recorded waveforms of terminal voltage and current but not the insights and inputs needed for inner behavior analysis and design optimization. This paper introduces lumped-parameter multi-physics models to describe different phases of arc behavior and outlines a methodology for model–data assimilation. Using experimental test data, the approach enables performance evaluation and supports non-invasive diagnostics and potential condition monitoring of circuit breakers. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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14 pages, 724 KiB

Open AccessArticle

Design and Uncertainty Evaluation of a Calibration Setup for Turbine Blades Vibration Measurement

by Lorenzo Capponi, Giulio Tribbiani, Vittoria Medici, Sara Fabri, Andrea Prato, Paolo Castellini, Alessandro Schiavi, Nicola Paone and Gianluca Rossi

Sensors 2024, 24(24), 8050; https://doi.org/10.3390/s24248050 - 17 Dec 2024

Viewed by 790

Abstract

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as [...] Read more.

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as an advanced alternative to traditional measurement methods, capturing blade dynamics by detecting deviations in blade tip arrival times through sensors mounted on the stator casing. This research focuses on developing an analytical model to quantify the uncertainty budget involved in designing a calibration setup for BTT systems, ensuring targeted performance levels. Unlike existing approaches, the proposed model integrates both operational variability and sensor performance characteristics, providing a comprehensive framework for uncertainty quantification. The model incorporates various operating and measurement scenarios to create an accurate and reliable calibration tool for BTT systems. In the broader context, this advancement supports the use of BTT for qualification processes, ultimately extending the lifespan of turbomachinery through condition-based maintenance. This approach enhances performance validation and monitoring in power plants and aircraft engines, contributing to safer and more efficient operations. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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19 pages, 6714 KiB

Open AccessArticle

Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network

by Zhunan Shen, Xiangwei Kong, Liu Cheng, Rengen Wang and Yunpeng Zhu

Sensors 2024, 24(4), 1290; https://doi.org/10.3390/s24041290 - 17 Feb 2024

Cited by 6 | Viewed by 1717

Abstract

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features [...] Read more.

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features that are not controllable and have poor interpretability. To this end, this study proposes an innovative semi-supervised learning method for bearing fault diagnosis. Firstly, multi-scale dilated convolution squeeze-and-excitation residual blocks are designed to exact local and global features. Secondly, a classifier generative adversarial network is employed to achieve multi-task learning. Both unsupervised and supervised learning are performed simultaneously to improve the generalization ability. Finally, supervised learning is applied to fine-tune the final model, which can extract multi-scale features and be further improved by implicit data augmentation. Experiments on two datasets were carried out, and the results verified the superiority of the proposed method. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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22 pages, 5486 KiB

Open AccessArticle

An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction

by Fengxia He, Chuansheng Zheng, Chao Pang, Chengying Zhao, Mingyang Yang, Yunpeng Zhu, Zhong Luo, Haitao Luo, Lei Li and Haotian Jiang

Sensors 2024, 24(3), 951; https://doi.org/10.3390/s24030951 - 1 Feb 2024

Cited by 3 | Viewed by 1553

Abstract

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope [...] Read more.

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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15 pages, 5738 KiB

Open AccessArticle

An Investigation into the Application of Acceleration Responses’ Trendline for Bridge Damage Detection Using Quadratic Regression

by Hadi Kordestani, Chunwei Zhang and Ali Arab

Sensors 2024, 24(2), 410; https://doi.org/10.3390/s24020410 - 9 Jan 2024

Cited by 17 | Viewed by 1618

Abstract

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply [...] Read more.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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23 pages, 6849 KiB

Open AccessArticle

A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images

by Jianan Cao, Yue Gao and Chuanyang Wang

Sensors 2023, 23(22), 9030; https://doi.org/10.3390/s23229030 - 7 Nov 2023

Cited by 1 | Viewed by 2451

Abstract

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, [...] Read more.

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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14 pages, 3395 KiB

Open AccessArticle

Quantification of Construction Materials Quality via Frequency Response Measurements: A Mobile Testing Station

by Lukasz Scislo and Nina Szczepanik-Scislo

Sensors 2023, 23(21), 8884; https://doi.org/10.3390/s23218884 - 1 Nov 2023

Cited by 7 | Viewed by 1935

Abstract

In construction, ensuring the quality and compliance of materials with specified requirements is often challenging, especially at construction sites. Conventionally, this process necessitates transporting samples to well-equipped laboratories, incurring significant time and financial costs. This article proposes a novel approach through a cost-effective [...] Read more.

In construction, ensuring the quality and compliance of materials with specified requirements is often challenging, especially at construction sites. Conventionally, this process necessitates transporting samples to well-equipped laboratories, incurring significant time and financial costs. This article proposes a novel approach through a cost-effective mobile test station, enabling on-site measurements and immediate evaluation results, regardless of the testing conditions. The foundation of our testing methodology lies in the Impulse Excitation Technique (IET), which capitalises on measuring the frequency response of samples while considering their mass and dimensions. By applying this technique, we can effectively determine crucial elastic properties, such as the Young Modulus and Poisson Ratio. These obtained values can then be cross-referenced with established material tables to verify the material’s compliance with the specified order. In this study, the developed universal and mobile test station demonstrated versatility by successfully evaluating three samples of typical construction materials, showing the method’s reliability on some real case measurements. The results substantiate its potential as a reliable mobile quality assurance station. Moreover, the station’s adaptability empowers its use on site, in laboratory settings, or even during material transportation when necessary. This innovation promises to revolutionise material quality assessment, streamlining the construction process and expediting decision making. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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14 pages, 5286 KiB

Open AccessArticle

Detection and Identification for Void of Concrete Structure by Air-Coupled Impact-Echo Method

by Jinghui Ju, Xiushu Tian, Weigang Zhao and Yong Yang

Sensors 2023, 23(13), 6018; https://doi.org/10.3390/s23136018 - 29 Jun 2023

Cited by 4 | Viewed by 2450

Abstract

In the field of non-destructive testing (NDT) for concrete structures, the traditional air-coupled impact-echo technology often has the problems of complex operation and low efficiency. In order to solve these problems, this study uses Comsol software to establish a finite element model (FEM) [...] Read more.

In the field of non-destructive testing (NDT) for concrete structures, the traditional air-coupled impact-echo technology often has the problems of complex operation and low efficiency. In order to solve these problems, this study uses Comsol software to establish a finite element model (FEM) of the concrete structure with different void sizes and obtains the variation rule of peak frequency. The recognition property of the concrete void based on peak frequency is proposed, which is explained and validated by relevant theory and experiments. The results show that compared with the depth of the void, the influence of the void width on the peak frequency increases significantly. When the void width is greater than 0.3 m, the peak frequency of the sound wave decreases with the increase in the width, and the change is obvious. This paper describes the applicability of concrete void depth less than 0.4 m for the air-coupled method and, when the concrete void depth is less than 0.4 m, the peak frequency can be used to effectively identify void widths greater than 0.3 m. The research results will be beneficial to void detection of concrete structures such as tunnel lining and pavements. Full article

(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)

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