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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 (31 August 2022) | Viewed by 25146

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Special Issue Editors

Prof. Dr. Yongbo Li

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Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modeling of mechanical systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bing Li

E-Mail Website
Guest Editor

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: metamaterials; metasurface; elastic wave; vibration attenuation
Special Issues, Collections and Topics in MDPI journals

Dr. Jinchen Ji

E-Mail Website
Guest Editor

School of Mechanical and Mechatronic Engineering, Faculty of Engineering and IT, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia
Interests: dynamic modelling; vibration analysis; vibration control; fatigue analysis; stability analysis; FEM analysis; cooperative control of multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Dr. Hamed Kalhori

E-Mail Website
Guest Editor

1. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
2. Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65167-38695, Iran
Interests: structural health monitoring; inverse problems; sensors and signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit papers to this Special Issue of Sensors on “Advanced Sensing, Fault Diagnostics, and Structural Health Management”. Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industry. In view of the current state of the art and advances in this fast-growing discipline, in this Special Issue, we are calling for papers related to all aspects of fault diagnostics, damage identification, and prognostics-based health management. A wide range of topics are covered, including new theories, methodologies, optimization, and applications in sensing, measurement, modeling, control, and prognostics. Topics include but are not limited to:

Measuring techniques for condition monitoring;
Reliability analysis and design;
Signal processing of measured data;
Feature extraction of measured data;
Fault diagnosis for prognosis and health management (PHM);
Degradation modeling of measured data;;
Measurement error analysis;
RUL prediction method based on intelligent algorithms;
Maintenance strategy optimization;
Structural health monitoring (SHM);
Non-destructive testing (NDT).

Dr. Yongbo Li
Dr. Bing Li
Dr. Jinchen Ji
Dr. Hamed Kalhori
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

fault diagnostics
prognostics and health management (PHM)
condition monitoring
information theory
signal processing
dynamic modeling
feature extraction
SHM
NDT

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Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 163 KiB

Open AccessEditorial

Advanced Sensing, Fault Diagnostics, and Structural Health Management

by Yongbo Li, Bing Li, Jinchen Ji and Hamed Kalhori

Sensors 2022, 22(23), 9087; https://doi.org/10.3390/s22239087 - 23 Nov 2022

Viewed by 1329

Abstract

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries [...] Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

Research

Jump to: Editorial

16 pages, 29415 KiB

Open AccessArticle

A Robust Faster R-CNN Model with Feature Enhancement for Rust Detection of Transmission Line Fitting

by Zhimin Guo, Yangyang Tian and Wandeng Mao

Sensors 2022, 22(20), 7961; https://doi.org/10.3390/s22207961 - 19 Oct 2022

Cited by 15 | Viewed by 2129

Abstract

Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (UAV). Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. To improve the detection reliability and robustness, this paper proposes a new robust Faster R-CNN model with feature enhancement mechanism for the rust detection of transmission line fitting. Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. Through calculating the similarity between each region proposal and the others, the feature weights of the region proposals containing target object can be enhanced via the overlaying of the object’s representation. The weight of the disturbance terms can then be relatively reduced. Empirical evaluation is conducted on some real-world UAV monitoring images. The comparative results demonstrate the effectiveness of the proposed model in terms of detection precision and recall rate, with the average precision of rust detection 97.07%, indicating that the proposed method can provide an reliable and robust solution for the rust detection. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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18 pages, 1151 KiB

Open AccessArticle

A Data-Driven OBE Magnetic Interference Compensation Method

by Yizhen Wang, Qi Han, Dechen Zhan and Qiong Li

Sensors 2022, 22(20), 7732; https://doi.org/10.3390/s22207732 - 12 Oct 2022

Cited by 3 | Viewed by 1752

Abstract

Aeromagnetic compensation is a technology used to reduce aircraft magnetic interference, which plays an important role in aeromagnetic surveys. In addition to maneuvering interferences, the onboard electronic (OBE) interference has been proven to be a significant part of aircraft interference, which must be reduced before further interpretation of aeromagnetic data. In the past, most researchers have focused on establishing linear models to compensate for OBE magnetic interference. However, such methods can only work using accurate reference sensors. In this paper, we propose a data-driven OBE interference compensation method, which can reduce OBE interference without relying on any other reference sensor. This network-based method can integrally detect and repair the OBE magnetic interference. The proposed method builds a prediction model by combining wavelet decomposition with a long short-term memory (LSTM) network to detect and predict OBE interference, and then estimates the local variation of the magnetic field to remove the drift of the interference. In our tests, we construct 10 semi-real datasets to quantitatively evaluate the performance of the proposed method. The F1 score of the proposed method for OBE interference detection is over

0.79

, and the RMSE of the compensated signal is less than

0.009

nT. Moreover, we also test our method on real signals, and the results show that our method can detect all interference and significantly reduce the standard deviation of the magnetic field. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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16 pages, 7669 KiB

Open AccessArticle

Damage Monitoring of Engineered Cementitious Composite Beams Reinforced with Hybrid Bars Using Piezoceramic-Based Smart Aggregates

by Hui Qian, Yuqing Zhang, Yuechang Li, Jundong Gao and Jianxue Song

Sensors 2022, 22(19), 7184; https://doi.org/10.3390/s22197184 - 22 Sep 2022

Cited by 7 | Viewed by 1835

Abstract

In order to explore the crack development mechanism and damage self-repairing capacity of ECC beams reinforced with hybrid bars, the smart aggregate-based active sensing approach were herein adopted to conduct damage monitoring of ECC beams under cyclic loading. A total of six beams, including five engineered cementitious composite (ECC) beams reinforced with different bars and one reinforcement concrete counterpart, were fabricated and tested under cyclic loading. The ultimate failure modes and hysteresis curves were obtained and discussed herein, demonstrating the multiple crack behavior and excellent ductility of ECC material. The damage of the tested beams was monitored by smart aggregate-based (SA) active sensing method, in which two SAs pasted on both beam ends were used as actuator and sensor, respectively. The time domain analysis, wavelet packet-based energy analysis and wavelet packet-based damage index analysis were performed to quantitatively evaluate the crack development. To evaluate the self-repairing capacity of the beams, a self-repairing index defined by the difference of damage index at loading and unloading peak points was proposed. The results in time domain and wavelet packed analysis were in close agreement with the observed crack development, revealing the feasibility of smart aggregate-based active sensing approach in damage detection for ECC beams. Especially, the proposed damage self-repairing index can describe the same structural re-centering phenomena with the test results, showing the proposed index can be used to evaluate the damage self-repairing capacity. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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16 pages, 5001 KiB

Open AccessArticle

A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables

by Yanwen Wang, Peng Chen, Yanying Sun and Chen Feng

Sensors 2022, 22(19), 7174; https://doi.org/10.3390/s22197174 - 21 Sep 2022

Cited by 6 | Viewed by 1551

Abstract

At present, the online insulation monitoring and fault diagnosis of mining cables are extensively discussed, while their operation status assessment has not been deeply studied. Considering that mining cables are closely related to the safe and stable operation of coal mine power supply systems, a comprehensive evaluation method including the Analytic Hierarchy Process (AHP), the membership cloud theory, and the D-S evidence theory is proposed in this paper in order to accurately assess the operation status of the mining XLPE cable. Firstly, the membership cloud is introduced to solve the index membership degree and the weights are calculated by an improved weight vector calculation method. Secondly, the conversion from the base layer indicator membership degree to the target layer trust degree is realized based on the D-S evidence theory. Then, the cable operation status is judged via the trust degree maximum and the distribution of conflict coefficients is further analyzed to warn the indicators with a bad status in the base layer. Finally, the feasibility of the proposed evaluation method is verified by a sufficient and detailed case analysis. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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13 pages, 12465 KiB

Open AccessArticle

New Technique for Impact Calibration of Wide-Range Triaxial Force Transducer Using Hopkinson Bar

by Qinghua Wang, Feng Xu, Weiguo Guo and Meng Gao

Sensors 2022, 22(13), 4885; https://doi.org/10.3390/s22134885 - 28 Jun 2022

Cited by 6 | Viewed by 1605

Abstract

At the current stage, there is an urgent need for new techniques to dynamically calibrate advanced wide-range (up to 10⁴ N~10⁵ N) triaxial force transducers. Based on this background, this paper proposes a novel impact calibration method, specifically for the triaxial force transducer, with a wide range and high-frequency response. In this method, the Hopkinson bar, which is typically used to test the dynamic mechanical properties of materials, was used as a generator to generate reference input force for the transducer. In addition, unlike conventional methods, the transverse sensitivities of the transducer were given necessary importance in the proposed method. The calibration result of the triaxial force transducer was expressed in a sensitivity matrix, containing three main sensitivity coefficients and six transverse sensitivity coefficients. The least squares method (LSM) was used to fit the sensitivity matrix linearly. Calibration experiments were performed on a typical triaxial force transducer. Several key issues, involving the validity and the test range, of the method were further investigated numerically. The feasibility and validity of the method were eventually confirmed. The test range of the method can be up to 10⁶ N. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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19 pages, 4237 KiB

Open AccessArticle

A New Axial Stress Measurement Method for High-Strength Short Bolts Based on Stress-Dependent Scattering Effect and Energy Attenuation Coefficient

by Tong Fu, Ping Chen and Aijun Yin

Sensors 2022, 22(13), 4692; https://doi.org/10.3390/s22134692 - 22 Jun 2022

Cited by 4 | Viewed by 2022

Abstract

The accurate estimation of axial stresses is a major problem for high-strength bolted connections that needs to be overcome to improve the assembly quality and safety of aviation structures. However, the conventional acoustoelastic effect based on velocity-stress dependence is very weak for short bolts, which leads to large estimation errors. In this article, the effect of axial stress on ultrasonic scattering attenuation is investigated by calculating the change in the energy attenuation coefficient of ultrasonic echoes after applying axial preload. Based on this effect, a stress-dependent attenuation estimation model is developed to measure the bolt axial stress. In addition, the spectrum of the first and second round-trip echoes is divided into several frequency bands to calculate the energy attenuation coefficients, which are used to select the frequency band sensitive to the axial stress changes. Finally, the estimation model between axial stress and energy attenuation coefficients in the sensitive frequency band is established under 20 steps of axial preloads. The experimental results show that the energy attenuation coefficient in the sensitive band corresponds well with axial stress. The average relative error of the predicted axial stress is 6.28%, which is better than that of the conventional acoustoelastic effect method. Therefore, the proposed approach can be used as an effective method to measure the axial stress of short bolts in the assembly of high-strength connections. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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21 pages, 10854 KiB

Open AccessArticle

A Crack Propagation Method for Pipelines with Interacting Corrosion and Crack Defects

by Mingjiang Xie, Yifei Wang, Weinan Xiong, Jianli Zhao and Xianjun Pei

Sensors 2022, 22(3), 986; https://doi.org/10.3390/s22030986 - 27 Jan 2022

Cited by 12 | Viewed by 2539

Abstract

Corrosion and crack defects often exist at the same time in pipelines. The interaction impact between these defects could potentially affect the growth of the fatigue crack. In this paper, a crack propagation method is proposed for pipelines with interacting corrosion and crack defects. The finite element models are built to obtain the Stress Intensity Factors (SIFs) for fatigue crack. SIF interaction impact ratio is introduced to describe the interaction effect of corrosion on fatigue crack. Two approaches based on extreme gradient boosting (XGBoost) are proposed in this paper to predict the SIF interaction impact ratio at the deepest point of the crack defect for pipelines with interacting corrosion and crack defects. Crack size, corrosion size and the axial distance between these two defects are the factors that have an impact on the growth of the fatigue crack, and so they are considered as the input of XGBoost models. Based on the synthetic samples from finite element modeling, it has been proved that the proposed approaches can effectively predict the SIF interaction impact ratio with relatively high accuracy. The crack propagation models are built based on the proposed XGBoost models, Paris’ law and corrosion growth model. Sensitivity analyses regarding corrosion initial depth and axial distance between defects are performed. The proposed method can support pipeline integrity management by linking the crack propagation model with corrosion size, crack size and the axial distance. The problem of how the interaction between corrosion and crack defects impacts crack defect growth is investigated. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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25 pages, 8192 KiB

Open AccessArticle

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

by Ran Duan, Jie Liu, Jianzhong Zhou, Pei Wang and Wei Liu

Sensors 2022, 22(2), 525; https://doi.org/10.3390/s22020525 - 11 Jan 2022

Cited by 8 | Viewed by 1897

Abstract

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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14 pages, 2643 KiB

Open AccessArticle

Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment

by Kangyu Zhang, Kuan Lu, Xiaohui Gu, Chao Fu and Shibo Zhao

Sensors 2022, 22(1), 62; https://doi.org/10.3390/s22010062 - 23 Dec 2021

Cited by 8 | Viewed by 3376

Abstract

In recent years, Tethered Space Systems (TSSs) have received significant attention in aerospace research as a result of their significant advantages: dexterousness, long life cycles and fuel-less engines. However, configurational conversion processes of tethered satellite formation systems in a complex space environment are essentially unstable. Due to their structural peculiarities and the special environment in outer space, TSS vibrations are easily produced. These types of vibrations are extremely harmful to spacecraft. Hence, the nonlinear dynamic behavior of systems based on a simplified rigid-rod tether model is analyzed in this paper. Two stability control laws for tether release rate and tether tension are proposed in order to control tether length variation. In addition, periodic stability of time-varying control systems after deployment is analyzed by using Floquet theory, and small parameter domains of systems in asymptotically stable states are obtained. Numerical simulations show that proposed tether tension controls can suppress in-plane and out-of-plane librations of rigid tethered satellites, while spacecraft and tether stability control goals can be achieved. Most importantly, this paper provides tether release rate and tether tension control laws for suppressing wide-ranging TSS vibrations that are valuable for improving TSS attitude control accuracy and performance, specifically for TSSs that are operating in low-eccentricity orbits. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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12 pages, 3915 KiB

Open AccessArticle

Development of a Wireless Corrosion Detection System for Steel-Framed Structures Using Pulsed Eddy Currents

by Namhoon Ha, Han-Seung Lee and Songjun Lee

Sensors 2021, 21(24), 8199; https://doi.org/10.3390/s21248199 - 8 Dec 2021

Cited by 8 | Viewed by 3212

Abstract

Structural health monitoring (SHM) can be more efficient with the application of a wireless sensor network (WSN). However, the hardware that makes up this system should have sufficient performance to sample the data collected from the sensor in real-time situations. High-performance hardware can be used for this purpose, but is not suitable in this application because of its relatively high power consumption, high cost, large size, and so on. In this paper, an optimal remote monitoring system platform for SHM is proposed based on pulsed eddy current (PEC) that is utilized for measuring the corrosion of a steel-framed construction. A circuit to delay the PEC response based on the resistance–inductance–capacitance (RLC) combination was designed for data sampling to utilize the conventional hardware of WSN for SHM, and this approach was verified by simulations and experiments. Especially, the importance of configuring sensing modules and the WSN for remote monitoring were studied, and the PEC responses caused by the corrosion of a specimen made with steel were able to be sampled remotely using the proposed system. Therefore, we present a remote SHM system platform for diagnosing the corrosion condition of a building with a steel structure, and proving its viability with experiments. Full article

(This article belongs to the Special Issue Advanced Sensing, Fault Diagnostics, and Structural Health Management)

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