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Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis: 2nd Edition

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 2256

Special Issue Editors

Dr. Zijian Qiao

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China
Interests: mechanical fault diagnosis; weak signal detection; structural health monitoring; intelligent medical equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Zhihui Lai

E-Mail Website
Guest Editor
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: mechanical system dynamics; equipment monitoring and diagnosis; vibration energy harvesting; nonlinear vibration reduction/isolation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) aims to identify the damage caused to fixed objects, including aerospace, civil, and mechanical engineering infrastructure, whereas mechanical fault diagnosis (MFD) seeks to monitor the health states and diagnose the damage to rotating objects, including wind turbines, aero-engines, and high-speed trains. These methods have attracted sustained and growing interest. However, it is vital for investigators to use advanced and intelligent sensors to acquire accurate and multi-source data from fixed and rotating objects. Moreover, the data quality is highly dependent on the sensors. Up to now, many scholars have applied advanced sensors to SHM and MFD, including acoustic emissions, vibration, strain, temperature, images, audio, electric currents, chemical analysis, optical fibers, oil analysis sensors, etc.

This Special Issue therefore aims to compile original research and review articles on the recent advances, technologies, solutions, applications, and new challenges in the field of intelligent sensors for SHM and MFD. These topics include, but are not limited to, the following:

  • Novel sensors and sensing technologies in SHM and MFD;
  • Intelligent SHM and MFD methods;
  • Improved and enhanced data quality methods in SHM and MFD;
  • Advanced signal processing techniques in SHM and MFD;
  • Sensor network design and optimization in SHM and MFD;
  • Remaining useful life prediction in SHM and MFD;
  • Weak signal detection and enhancement in SHM and MFD;
  • Built-in SHM and MFD intelligent maintenance and health management.

Dr. Zijian Qiao
Dr. Zhihui Lai
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 250 words) can be sent to the Editorial Office for assessment.

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

  • intelligent sensing
  • advanced sensors
  • structural health monitoring
  • mechanical fault diagnosis
  • data quality improvement

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Related Special Issue

Published Papers (4 papers)

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Research

26 pages, 10963 KB  
Article
Noise-Resilient Whitened Domain Adaptation for Intelligent Mechanical Fault Diagnosis Under Non-Stationary Sensor Signals
by Qinyue Chen and Yunxin Xie
Sensors 2026, 26(10), 3222; https://doi.org/10.3390/s26103222 - 19 May 2026
Abstract
Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while [...] Read more.
Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the P0→P3 transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis: 2nd Edition)
21 pages, 353 KB  
Article
FG-DeformT: Grouped Deformable Temporal Modeling for Flight-Level Aircraft Anomaly Detection
by Yinpan He, Zhen Lu, Yi Deng and Di Wang
Sensors 2026, 26(10), 3189; https://doi.org/10.3390/s26103189 - 18 May 2026
Viewed by 162
Abstract
Aircraft anomaly detection from multivariate telemetry time series is critical for flight safety and predictive maintenance. However, random window-level evaluation may overestimate performance because highly correlated windows from the same flight can appear in both the training and test sets. To obtain a [...] Read more.
Aircraft anomaly detection from multivariate telemetry time series is critical for flight safety and predictive maintenance. However, random window-level evaluation may overestimate performance because highly correlated windows from the same flight can appear in both the training and test sets. To obtain a stricter assessment of cross-flight generalization, this study adopts a leakage-aware flight-level validation protocol and proposes FG-DeformT, a grouped deformable Transformer for aircraft time-series anomaly detection. The model combines global temporal modeling with a grouped temporal offset mechanism to capture heterogeneous local temporal shifts across latent feature subspaces. Experiments on the ALFA dataset show that conventional baselines degrade sharply under flight-level evaluation, whereas FG-DeformT remains robust, achieving a Precision of 0.925, a Recall of 0.941, and an F1-score of 0.933 on the primary binary anomaly detection task. Ablation and downstream analyses further indicate that the grouped offset mechanism contributes to anomaly detection, while its benefit for fine-grained fault isolation is category-dependent rather than uniformly superior. External validation and computational analyses are included to further examine the framework’s applicability and practical feasibility. Overall, this study highlights the importance of leakage-aware flight-level evaluation and suggests that grouped temporal deformation offers a practical way to model heterogeneous temporal patterns in safety-critical telemetry data. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis: 2nd Edition)
16 pages, 2525 KB  
Article
Novel Technology for Unbalance Diagnosis for Dual-Speed Wind Turbines
by Amir R. Askari, Len Gelman, Russell King, Daryl Hickey and Mehdi Behzad
Sensors 2026, 26(7), 2268; https://doi.org/10.3390/s26072268 - 7 Apr 2026
Viewed by 1069
Abstract
Unbalance diagnosis for non-constant speed systems is challenging because the 1X fundamental rotational harmonic magnitude, commonly used as an unbalance indicator, depends on shaft rotational speed. This dependency makes it difficult to separate speed effects from unbalance effects. It has been shown that [...] Read more.
Unbalance diagnosis for non-constant speed systems is challenging because the 1X fundamental rotational harmonic magnitude, commonly used as an unbalance indicator, depends on shaft rotational speed. This dependency makes it difficult to separate speed effects from unbalance effects. It has been shown that 1X magnitudes become speed-invariant if they are normalized with respect to the rotational speed in power four for variable-speed wind turbines. However, the applicability of this diagnostic technology to dual-speed machines remains unclear. This study experimentally investigates unbalance diagnosis technologies for dual-speed wind turbines, for which speed-dependent interference is present. Vibration data are collected from the main bearings of two dual-speed wind turbines. Novel residual-based, speed-invariant unbalance diagnostic technology is proposed. The experimental results show consistent statistical distributions of the new diagnosis indicator across low and high-speed operating regimes. These findings confirm the suitability of the proposed technology for unbalance diagnosis for dual-speed rotating machinery. Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis: 2nd Edition)
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29 pages, 9489 KB  
Article
Lightweight Gearbox Fault Diagnosis Under High Noise Based on Improved Multi-Scale Depthwise Separable Convolution and Efficient Channel Attention
by Xiubin Liu, Wei Li, Haoming Li, Yong Zhu and Ramesh K. Agarwal
Sensors 2026, 26(4), 1196; https://doi.org/10.3390/s26041196 - 12 Feb 2026
Cited by 1 | Viewed by 526
Abstract
Gearbox fault diagnosis under strong-noise conditions remains challenging due to the difficulty of extracting weak fault-related features from noise-dominated vibration signals, inefficient modeling of multi-scale impulsive characteristics under limited computational resources, and degraded diagnostic stability across varying noise levels. To address these issues, [...] Read more.
Gearbox fault diagnosis under strong-noise conditions remains challenging due to the difficulty of extracting weak fault-related features from noise-dominated vibration signals, inefficient modeling of multi-scale impulsive characteristics under limited computational resources, and degraded diagnostic stability across varying noise levels. To address these issues, this paper proposes a lightweight fault diagnosis model (DSMC-ECA) that integrates an improved multi-scale depthwise separable convolution scheme with efficient channel attention. The proposed model adopts a dual-branch parallel feature extraction architecture: the SMC branch captures local fine-grained impulsive features, while the SMDC branch expands the receptive field via multi-scale separable dilated convolutions to model long-range dependencies. Meanwhile, ECA is embedded into the multi-scale features for channel-wise recalibration, highlighting fault-relevant discriminative information and suppressing noise disturbances. The model contains only 0.204 M parameters and requires 10.037 M FLOPs, achieving a favorable trade-off between performance and efficiency. Experimental results on the XJTU and SEU datasets demonstrate that DSMC-ECA consistently outperforms baseline methods across a wide range of signal-to-noise ratios (from −6 dB to noise-free conditions). Notably, under the most challenging −6 dB setting, it achieves the highest average diagnostic accuracies of 95.11% (XJTU) and 86.84% (SEU). Full article
(This article belongs to the Special Issue Intelligent Sensors for Structural Health Monitoring and Mechanical Fault Diagnosis: 2nd Edition)
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