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Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics

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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 March 2024) | Viewed by 27213

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Prof. Dr. Bing Li

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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

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

Dr. Khandaker Noman

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Guest Editor

School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: predictive maintenance; digital twin; signal processing; machine learning; system reliability analysis; remaining useful life prediction; time–frequency analysis
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 “Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics”. Over recent decades, the field of Structural Health Monitoring (SHM) has been experiencing spectacular development. The applications have expanded from aerospace, mechanical, and civil engineering, now to essentially all other types of fundamental issues, including those in the nuclear, marine, and wind turbine industries. In particular, with the rapid development of advanced sensing, data-driven intelligence algorithms, and innovative in situ diagnostic and prognostic strategies, significant advances and wide fascinating technical possibilities have been reported in this area. These studies will doubtlessly promote the reliability, availability, and robustness of the systems of SHM and condition monitoring. In view of this highly active research area at present, we are pleased to invite researchers to contribute original research papers, as well as review papers related to all aspects of advanced sensing, fault diagnostics, intelligence algorithms, and prognostics-based health monitoring and 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, the following:

Structural health monitoring (SHM);
Non-destructive testing (NDT);
Prognosis and health management (PHM);
Self-diagnostics, prognostics, condition-based maintenance; and performance;
Vibration and wave propagation methods for damage assessment;
Reliability analysis and design;
Feature extraction and signal processing of measured data;
Damage identification based on artificially structured materials;
Monitoring of composite, metallic, new, and aging structures and infrastructure;
Measuring techniques for condition monitoring;
RUL prediction method based on intelligent algorithms.

Prof. Dr. Bing Li
Dr. Yongbo Li
Dr. Khandaker Noman
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

structural health monitoring (SHM)
non-destructive testing (NDT)
prognostics and health management (PHM)
advanced sensing
fault diagnostics
condition monitoring
signal processing
feature extraction

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Published Papers (13 papers)

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Research

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28 pages, 30269 KiB

Open AccessArticle

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data

by Doron Hekič, Diogo Ribeiro, Andrej Anžlin, Aleš Žnidarič and Peter Češarek

Sensors 2024, 24(9), 2788; https://doi.org/10.3390/s24092788 - 27 Apr 2024

Viewed by 868

Abstract

Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young’s modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure’s design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25–50% overestimated design stiffness of internal main girders. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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19 pages, 7951 KiB

Open AccessArticle

Spatial Galloping Behavior of Iced Conductors under Multimodal Coupling

by Fujiang Cui, Kaihong Zheng, Peng Liu and Han Wang

Sensors 2024, 24(3), 784; https://doi.org/10.3390/s24030784 - 25 Jan 2024

Cited by 3 | Viewed by 945

Abstract

In this study, the coupled ordinary differential equations for the galloping of the first two modes in iced bundled conductors, including in-plane, out-of-plane, and torsional directions, are derived. Furthermore, through numerical analysis, the critical conditions of this modal galloping are determined in the range of wind speed–sag parameters, and the galloping patterns and variation laws in different parameter spaces are analyzed. The parameter space is then divided into five regions according to the different galloping modes. Under the multimodal coupling mechanism of galloping, the impact of single and two kinds of coupled mode galloping on the spatial nonlinear behavior is explored. The results reveal that the system exhibits an elliptical orbit motion during single mode galloping, while an “8” motion pattern emerges during coupled mode galloping. Moreover, two patterns of “8” motion are displayed under different parameter spaces. This research provides a theoretical foundation for the design of transmission lines. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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16 pages, 2362 KiB

Open AccessArticle

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization

by Kairong Hong, Yingying Ren, Fengyuan Li, Wentao Mao and Xiang Gao

Sensors 2023, 23(16), 7182; https://doi.org/10.3390/s23167182 - 15 Aug 2023

Cited by 1 | Viewed by 1837

Abstract

Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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21 pages, 5660 KiB

Open AccessArticle

Wavelet-Based Output-Only Damage Detection of Composite Structures

by Rims Janeliukstis and Deniss Mironovs

Sensors 2023, 23(13), 6121; https://doi.org/10.3390/s23136121 - 3 Jul 2023

Viewed by 1161

Abstract

Health monitoring of structures operating in ambient environments is performed through operational modal analysis, where the identified modal parameters, such as resonant frequencies, damping ratios and operation deflection shapes, characterize the state of structural integrity. The current study shows that, first, time-frequency methods, such as continuous wavelet transform, can be used to identify these parameters and may even provide a large amount of such data, increasing the reliability of structural health monitoring systems. Second, the identified resonant frequencies and damping ratios are used as features in a damage-detection scheme, utilizing the kernel density estimate (KDE) of an underlying probability distribution of features. The Euclidean distance between the centroids of the KDEs, at reference and in various other cases of structural integrity, is used as an indicator of deviation from reference. Validation of the algorithm was carried out in a vast experimental campaign on glass fibre-reinforced polymer samples with a cylindrical shell structure subjected to varying degrees of damage. The proposed damage indicator, when compared with the well-known Mahalanobis distance metric, yielded comparable damage detection accuracy, while at the same time being not only simpler to calculate but also able to capture the severity of damage. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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13 pages, 2837 KiB

Open AccessArticle

Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling Method

by Hailin Lu, Dongchen Sun and Jing Hao

Sensors 2023, 23(5), 2795; https://doi.org/10.3390/s23052795 - 3 Mar 2023

Cited by 3 | Viewed by 1653

Abstract

The rationality of heavy vehicle models is crucial to the structural safety assessment of bridges. To establish a realistic heavy vehicle traffic flow model, this study proposes a heavy vehicle random traffic flow simulation method that fully considers the vehicle weight correlation based on the measured weigh-in-motion data. First, a probability model of the key parameters in the actual traffic flow is established. Then, a random traffic flow simulation of heavy vehicles is realized using the R-vine Copula model and improved Latin hypercube sampling (LHS) method. Finally, the load effect is calculated using a calculation example to explore the necessity of considering the vehicle weight correlation. The results indicate that the vehicle weight of each model is significantly correlated. Compared to the Monte Carlo method, the improved LHS method better considers the correlation between high-dimensional variables. Furthermore, considering the vehicle weight correlation using the R-vine Copula model, the random traffic flow generated by the Monte Carlo sampling method ignores the correlation between parameters, leading to a weaker load effect. Therefore, the improved LHS method is preferred. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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18 pages, 3304 KiB

Open AccessArticle

Crack Monitoring in Rotating Shaft Using Rotational Speed Sensor-Based Torsional Stiffness Estimation with Adaptive Extended Kalman Filters

by Young-Hun Park, Hee-Beom Lee and Gi-Woo Kim

Sensors 2023, 23(5), 2437; https://doi.org/10.3390/s23052437 - 22 Feb 2023

Cited by 3 | Viewed by 3032

Abstract

In this study, we present an alternative solution for detecting crack damages in rotating shafts under torque fluctuation by directly estimating the reduction in torsional shaft stiffness using the adaptive extended Kalman filter (AEKF) algorithm. A dynamic system model of a rotating shaft for designing AEKF was derived and implemented. An AEKF with a forgetting factor (λ) update was then designed to effectively estimate the time-varying parameter (torsional shaft stiffness) owing to cracks. Both simulation and experimental results demonstrated that the proposed estimation method could not only estimate the decrease in stiffness caused by a crack, but also quantitatively evaluate the fatigue crack growth by directly estimating the shaft torsional stiffness. Another advantage of the proposed approach is that it uses only two cost-effective rotational speed sensors and can be readily implemented in structural health monitoring systems of rotating machinery. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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20 pages, 7242 KiB

Open AccessArticle

Single-Sensor Engine Multi-Type Fault Detection

by Daijie Tang, Fengrong Bi, Jiangang Cheng, Xiao Yang, Pengfei Shen and Xiaoyang Bi

Sensors 2023, 23(3), 1642; https://doi.org/10.3390/s23031642 - 2 Feb 2023

Cited by 1 | Viewed by 1940

Abstract

Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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27 pages, 18377 KiB

Open AccessArticle

Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations

by Marta Berardengo, Francescantonio Lucà, Marcello Vanali and Gianvito Annesi

Sensors 2023, 23(3), 1154; https://doi.org/10.3390/s23031154 - 19 Jan 2023

Cited by 7 | Viewed by 1770

Abstract

Vibration-based damage features are widely adopted in the field of structural health monitoring (SHM), and particularly in the monitoring of axially loaded beams, due to their high sensitivity to damage-related changes in structural properties. However, changes in environmental and operating conditions often cause damage feature variations which can mask any possible change due to damage, thus strongly affecting the effectiveness of the monitoring strategy. Most of the approaches proposed to tackle this problem rely on the availability of a wide training dataset, accounting for the most part of the damage feature variability due to environmental and operating conditions. These approaches are reliable when a complete training set is available, and this represents a significant limitation in applications where only a short training set can be used. This often occurs when SHM systems aim at monitoring the health state of an already existing and possibly already damaged structure (e.g., tie-rods in historical buildings), or for systems which can undergo rapid deterioration. To overcome this limit, this work proposes a new damage index not affected by environmental conditions and able to properly detect system damages, even in case of short training set. The proposed index is based on the principal component analysis (PCA) of vibration-based damage features. PCA is shown to allow for a simple filtering procedure of the operating and environmental effects on the damage feature, thus avoiding any dependence on the extent of the training set. The proposed index effectiveness is shown through both simulated and experimental case studies related to an axially loaded beam-like structure, and it is compared with a Mahalanobis square distance-based index, as a reference. The obtained results highlight the capability of the proposed index in filtering out the temperature effects on a multivariate damage feature composed of eigenfrequencies, in case of both short and long training set. Moreover, the proposed PCA-based strategy is shown to outperform the benchmark one, both in terms of temperature dependency and damage sensitivity. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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19 pages, 8266 KiB

Open AccessArticle

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

by Deyu Zhuang, Hongrui Liu, Hao Zheng, Liang Xu, Zhengyang Gu, Gang Cheng and Jinbo Qiu

Sensors 2023, 23(2), 991; https://doi.org/10.3390/s23020991 - 15 Jan 2023

Cited by 17 | Viewed by 2329

Abstract

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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16 pages, 4305 KiB

Open AccessArticle

Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data

by Jun-Kyu Park, Howon Lee, Woojin Kim, Gyu-Man Kim and Dawn An

Sensors 2023, 23(2), 637; https://doi.org/10.3390/s23020637 - 5 Jan 2023

Cited by 1 | Viewed by 1654

Abstract

Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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12 pages, 1994 KiB

Open AccessArticle

High Precision Feature Fast Extraction Strategy for Aircraft Attitude Sensor Fault Based on RepVGG and SENet Attention Mechanism

by Zhen Jia, Kai Wang, Yang Li, Zhenbao Liu, Jian Qin and Qiqi Yang

Sensors 2022, 22(24), 9662; https://doi.org/10.3390/s22249662 - 9 Dec 2022

Cited by 10 | Viewed by 1798

Abstract

The attitude sensor of the aircraft can give feedback on the perceived flight attitude information to the input of the flight controller to realize the closed-loop control of the flight attitude. Therefore, the fault diagnosis of attitude sensors is crucial for the flight safety of aircraft, in view of the situation that the existing diagnosis methods fail to give consideration to both the diagnosis rate and the diagnosis accuracy. In this paper, a fast and high-precision fault diagnosis strategy for aircraft sensor is proposed. Specifically, the aircraft’s dynamics model and the attitude sensor’s fault model are built. The SENet attention mechanism is used to allocate weights for the collected time-domain fault signals and transformed time-frequency signals, and then inject the fused feature signals with weights into the RepVGG based on the convolutional neural network structure for deep feature mining and classification. Experimental results show that the proposed method can achieve good precision speed tradeoff. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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20 pages, 13887 KiB

Open AccessArticle

A TCP Acceleration Algorithm for Aerospace-Ground Service Networks

by Canyou Liu, Jimin Zhao, Feilong Mao, Shuang Chen, Na Fu, Xin Wang and Yani Cao

Sensors 2022, 22(23), 9187; https://doi.org/10.3390/s22239187 - 26 Nov 2022

Viewed by 1373

Abstract

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment’s deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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Review

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29 pages, 1766 KiB

Open AccessReview

Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review

by Yanfang Yang, Lei Ding, Jinhua Xiao, Guinan Fang and Jia Li

Sensors 2022, 22(24), 9714; https://doi.org/10.3390/s22249714 - 11 Dec 2022

Cited by 15 | Viewed by 4931

Abstract

To implement Prognostics Health Management (PHM) for hydraulic pumps, it is very important to study the faults of hydraulic pumps to ensure the stability and reliability of the whole life cycle. The research on fault diagnosis has been very active, but there is a lack of systematic analysis and summary of the developed methods. To make up for this gap, this paper systematically summarizes the relevant methods from the two aspects of fault diagnosis and health management. In addition, in order to further facilitate researchers and practitioners, statistical and comparative analysis of the reviewed methods is carried out, and a future development direction is prospected. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Advanced Sensing, Diagnostics and Prognostics)

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