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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: 20 December 2024 | Viewed by 16787

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Special Issue Editors

Prof. Dr. Jiawei Xiang

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Guest Editor

College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Interests: fault diagnosis; structural health monitoring; signal processing; finite element analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Laihao Yang

E-Mail Website
Guest Editor

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: bio-inspired soft robotics; continuum robotics; intelligent sensing; intelligent control; intelligent damage detection; intelligent maintenance
Special Issues, Collections and Topics in MDPI journals

Dr. Anil Kumar

E-Mail Website
Guest Editor

College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Interests: condition monitoring; fault diagnosis; fault prognosis; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is the process of establishing a damage identification method for engineering infrastructure. It entails evaluating the presence of damage, locating the fault, estimating the seriousness of the problem, and lastly, predicting the structure's remaining useful life. A structure can be as big as a bridge stretching several miles and can be as tiny as a small component of a rolling element bearing. Structural health monitoring is a hot topic. It is carried out by mounting sensors on machines. Various sensors, such as acoustic sensors, accelerometers, themo-couples, current sensors, ultra-transonic sensors, etc., can be mounted depending on the application and type of structure. However, determining the health of a machine is exceedingly tough and difficult since the health deterioration process is extremely complicated and stochastic and requires rapid attention. Through this SI, we wish to collect the following application of the sensor in the areas of:

Advanced noise, vibration, harshness measurement methods to detect structural faults;
Artificial Intelligence, sensor fusion, signal processing and intelligence testing for health monitoring;
Advanced modeling techniques to provide relevant information to the monitoring system;
Vibro-acoustic analysis of critical machinery/structures;
System identification and modal analysis;
Remaining useful life;
Deep leaning;
Graph Neural Networks;
Other innovative applications and technologies for the application of sensors for structural health monitoring.

Prof. Dr. Jiawei Xiang
Dr. Laihao Yang
Dr. Anil Kumar
Guest Editors

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Published Papers (9 papers)

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Research

12 pages, 2311 KiB

Open AccessArticle

Explore Ultrasonic-Induced Mechanoluminescent Solutions towards Realising Remote Structural Health Monitoring

by Marilyne Philibert and Kui Yao

Sensors 2024, 24(14), 4595; https://doi.org/10.3390/s24144595 - 16 Jul 2024

Viewed by 1067

Abstract

Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). Propagating guided waves transmitted by a piezoelectric transducer attached to a structure induce elastic deformation that can be captured by elastico-ML. An ML coating composed of copper-doped zinc sulfide (ZnS:Cu) particles embedded in PVDF on a thin aluminium plate can be used to achieve the elastico-ML for the remote sensing of propagating guided waves. The simulation and experimental results indicated that a very high voltage would be required to reach the threshold pressure applied to the ML particles, which is about 1.5 MPa for ZnS particles. The high voltage was estimated to be 214 Vpp for surface waves and 750 Vpp for Lamb waves for the studied configuration. Several possible technical solutions are suggested for achieving ultrasonic-induced ML for future remote SHM systems. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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15 pages, 5867 KiB

Open AccessArticle

Identification of Shield Tunnel Segment Joint Opening Based on Annular Seam Pressure Monitoring

by Hongbin Xu, Qucheng Liu, Bingtian Li and Chuanrui Guo

Sensors 2024, 24(12), 3924; https://doi.org/10.3390/s24123924 - 17 Jun 2024

Cited by 3 | Viewed by 669

Abstract

Tunnels for subways and railways are a vital part of urban transportation systems, where shield tunneling using assembled segmental linings is the predominant construction approach. With increasing operation time and varying geological conditions, shield tunnels usually develop defects that compromise both structural integrity and operational safety. One common issue is the separation of segment joints that may cause water/mud penetration and corrosion. Existing inspection strategies can only detect openings after their occurrence, which cannot provide early warnings for predictive maintenance. To address this issue, this work proposes a multi-point seam contact pressure monitoring method for joint opening identification. It first derived the theoretical correlation between contact pressure distribution and segment opening; then, a finite element model was established to explore the stress and deformation responses under combined axial and bending loads. Finally, multi-point piezoelectric film sensors were implemented on a scaled segment model to validate the theoretical and numerical analyses. Results indicate that the multi-point monitoring method can effectively identify opening amounts at the segment joints with an average error of 8.8%, confirming the method’s feasibility. These findings support the use of this monitoring technique for early detection and assessment of joint openings in shield tunnels. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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29 pages, 12368 KiB

Open AccessArticle

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings

by Huiming Jiang, Yue Wu, Jing Yuan, Qian Zhao and Jin Chen

Sensors 2024, 24(12), 3762; https://doi.org/10.3390/s24123762 - 9 Jun 2024

Viewed by 852

Abstract

Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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18 pages, 6606 KiB

Open AccessArticle

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data

by Guikai Xiong, Na Cui, Jiepeng Liu, Yan Zeng, Hanxin Chen, Chengliang Huang and Hao Xu

Sensors 2024, 24(5), 1394; https://doi.org/10.3390/s24051394 - 21 Feb 2024

Viewed by 1014

Abstract

The registration of bridge point cloud data (PCD) is an important preprocessing step for tasks such as bridge modeling, deformation detection, and bridge health monitoring. However, most existing research on bridge PCD registration only focused on pairwise registration, and payed insufficient attention to multi-view registration. In addition, to recover the overlaps of unordered multiple scans and obtain the merging order, extensive pairwise matching and the creation of a fully connected graph of all scans are often required, resulting in low efficiency. To address these issues, this paper proposes a marker-free template-guided method to align multiple unordered bridge PCD to a global coordinate system. Firstly, by aligning each scan to a given registration template, the overlaps between all the scans are recovered. Secondly, a fully connected graph is created based on the overlaps and scanning locations, and then a graph-partition algorithm is utilized to construct the scan-blocks. Then, the coarse-to-fine registration is performed within each scan-block, and the transformation matrix of coarse registration is obtained using an intelligent optimization algorithm. Finally, global block-to-block registration is performed to align all scans to a unified coordinate reference system. We tested our framework on different bridge point cloud datasets, including a suspension bridge and a continuous rigid frame bridge, to evaluate its accuracy. Experimental results demonstrate that our method has high accuracy. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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20 pages, 12344 KiB

Open AccessArticle

Vehicle–Bridge Interaction Modelling Using Precise 3D Road Surface Analysis

by Maja Kreslin, Peter Češarek, Aleš Žnidarič, Darko Kokot, Jan Kalin and Rok Vezočnik

Sensors 2024, 24(2), 709; https://doi.org/10.3390/s24020709 - 22 Jan 2024

Cited by 1 | Viewed by 1457

Abstract

Uneven road surfaces are the primary source of excitation in the dynamic interaction between a bridge and a vehicle and can lead to errors in bridge weigh-in-motion (B-WIM) systems. In order to correctly reproduce this interaction in a numerical model of a bridge, it is essential to know the magnitude and location of the various roadway irregularities. This paper presents a methodology for measuring the 3D road surface using static terrestrial laser scanning and a numerical model for simulating vehicle passage over a bridge with a measured road surface. This model allows the evaluation of strain responses in the time domain at any bridge location considering different parameters such as vehicle type, lateral position and speed, road surface unevenness, bridge type, etc. Since the time domain strains are crucial for B-WIM algorithms, the proposed approach facilitates the analysis of the different factors affecting the B-WIM results. The first validation of the proposed methodology was carried out on a real bridge, where extensive measurements were performed using different sensors, including measurements of the road surface, the response of the bridge when crossed by a test vehicle and the dynamic properties of the bridge and vehicle. The comparison between the simulated and measured bridge response marks a promising step towards investigating the influence of unevenness on the results of B-WIM. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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23 pages, 7727 KiB

Open AccessArticle

Damage Identification in Cement-Based Structures: A Method Based on Modal Curvatures and Continuous Wavelet Transform

by Gloria Cosoli, Milena Martarelli, Alessandra Mobili, Francesca Tittarelli and Gian Marco Revel

Sensors 2023, 23(22), 9292; https://doi.org/10.3390/s23229292 - 20 Nov 2023

Cited by 2 | Viewed by 1519

Abstract

Modal analysis is an effective tool in the context of Structural Health Monitoring (SHM) since the dynamic characteristics of cement-based structures reflect the structural health status of the material itself. The authors consider increasing level load tests on concrete beams and propose a methodology for damage identification relying on the computation of modal curvatures combined with continuous wavelet transform (CWT) to highlight damage-related changes. Unlike most literature studies, in the present work, no numerical models of the undamaged structure were exploited. Moreover, the authors defined synthetic damage indices depicting the status of a structure. The results show that the I mode shape is the most sensitive to damages; indeed, considering this mode, damages cause a decrease of natural vibration frequency (up to approximately −67%), an increase of loss factor (up to approximately fivefold), and changes in the mode shapes morphology (a cuspid appears). The proposed damage indices are promising, even if the level of damage is not clearly distinguishable, probably because tests were performed after the load removal. Further investigations are needed to scale the methodology to in-field applications. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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18 pages, 5043 KiB

Open AccessArticle

A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data

by Yi Liu, Hang Xiang, Zhansi Jiang and Jiawei Xiang

Sensors 2023, 23(6), 3068; https://doi.org/10.3390/s23063068 - 13 Mar 2023

Cited by 8 | Viewed by 1780

Abstract

Intelligent fault diagnosis of roller bearings is facing two important problems, one is that train and test datasets have the same distribution, and the other is the installation positions of accelerometer sensors are limited in industrial environments, and the collected signals are often polluted by background noise. In the recent years, the discrepancy between train and test datasets is decreased by introducing the idea of transfer learning to solve the first issue. In addition, the non-contact sensors will replace the contact sensors. In this paper, a domain adaption residual neural network (DA-ResNet) model using maximum mean discrepancy (MMD) and a residual connection is constructed for cross-domain diagnosis of roller bearings based on acoustic and vibration data. MMD is used to minimize the distribution discrepancy between the source and target domains, thereby improving the transferability of the learned features. Acoustic and vibration signals from three directions are simultaneously sampled to provide more complete bearing information. Two experimental cases are conducted to test the ideas presented. The first is to verify the necessity of multi-source data, and the second is to demonstrate that transfer operation can improve recognition accuracy in fault diagnosis. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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19 pages, 31315 KiB

Open AccessArticle

An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise

by Bingbing Hu, Jiahui Tang, Jimei Wu and Jiajuan Qing

Sensors 2022, 22(17), 6570; https://doi.org/10.3390/s22176570 - 31 Aug 2022

Cited by 15 | Viewed by 4188

Abstract

With the continuous development of artificial intelligence, data-driven fault diagnosis methods are gradually attracting widespread attention. However, in practical industrial applications, noise in the working environment is inevitable. This leads to the fact that the performance of traditional intelligent diagnosis methods is hardly sufficient to satisfy the requirements. In this paper, a developed intelligent diagnosis framework is proposed to overcome this deficiency. The main contributions of this paper are as follows: Firstly, a fault diagnosis model is established using EfficientNet, which achieves optimal diagnosis performance with limited computing resources. Secondly, an attention mechanism is introduced into the basic model for accurately establishing the relationship between fault features and fault modes, while improving the diagnosis accuracy in complex noise environments. Finally, to explain the proposed method, the weights and features of the model are visualized, and further attempts are made to analyze the reasons for the high performance of the model. The comprehensive experiment results reveal the superiority of the proposed method in terms of accuracy and stability in comparison with other benchmark diagnosis approaches. The diagnostic accuracy under actual working conditions is 86.24%. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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20 pages, 3812 KiB

Open AccessArticle

Three-Stage Wiener-Process-Based Model for Remaining Useful Life Prediction of a Cutting Tool in High-Speed Milling

by Weichao Liu, Wen-An Yang and Youpeng You

Sensors 2022, 22(13), 4763; https://doi.org/10.3390/s22134763 - 24 Jun 2022

Cited by 9 | Viewed by 2465

Abstract

Tool condition monitoring can be employed to ensure safe and full utilization of the cutting tool. Hence, remaining useful life (RUL) prediction of a cutting tool is an important issue for an effective high-speed milling process-monitoring system. However, it is difficult to establish a mechanism model for the life decreasing process owing to the different wear rates in various stages of cutting tool. This study proposes a three-stage Wiener-process-based degradation model for the cutting tool wear estimation and remaining useful life prediction. Tool wear stages classification and RUL prediction are jointly addressed in this work in order to take full advantage of Wiener process, as this three-stage Wiener process definitely constitutes to describe the degradation processes at different wear stages, based on which the overall useful life can be accurately obtained. The numerical results obtained using extensive experiment indicate that the proposed model can effectively predict the cutting tool’s remaining useful life. Empirical comparisons show that the proposed model performs better than existing models in predicting the cutting tool RUL. Full article

(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)

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