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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: 31 August 2026 | Viewed by 3889

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Special Issue Editor

Prof. Dr. Peng Liu

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Guest Editor

School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Interests: intelligent sensing and measurement; sensor-based monitoring, diagnosis, and control; smart instruments for equipment operation and maintenance; sensing and data analytics in intelligent manufacturing systems

Special Issue Information

Dear Colleagues,

Industrial defect detection plays a crucial role in ensuring the reliability, safety, and sustainability of modern manufacturing systems. As industries move toward intelligent and autonomous production, sensing technologies have become fundamental to achieving precise, efficient, and automated quality control. Recent progress in sensing hardware, signal acquisition, and intelligent data analytics has significantly advanced the precision and automation of industrial inspection. The integration of multi-modal sensing, artificial intelligence, and adaptive monitoring systems is transforming defect detection from offline quality control to continuous, intelligent, and predictive inspection throughout the manufacturing lifecycle.

This Special Issue aims to bring together original research and review articles on recent advances, technologies, applications, and challenges in the field of industrial defect detection based on sensing technologies.

Prof. Dr. Peng Liu
Guest Editor

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Keywords

industrial defect detection
sensing technology
non-destructive testing
infrared thermography
ultrasonic sensing
acoustic sensing
machine vision
data fusion
intelligent inspection
smart manufacturing

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Published Papers (7 papers)

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Research

19 pages, 2352 KB

Open AccessArticle

Interval Prediction of Remaining Useful Life Based on Uncertainty Quantification with Bayesian Convolutional Neural Networks Featuring Dual-Output Units

by Zhendong Qu, Jialong He, Yan Liu, Song Mao and Xiaowu Han

Sensors 2026, 26(9), 2592; https://doi.org/10.3390/s26092592 - 22 Apr 2026

Viewed by 328

Abstract

RUL prediction methods do not fully account for the uncertainties caused by data scarcity and inherent noise, and they also suffer from low reliability of RUL point estimates. To tackle these challenges, this paper proposes a Bayesian convolutional neural network with dual-output units for RUL interval predictions. The network employs the negative log-likelihood as the loss function. Thanks to its dual-output structure, it not only provides point estimates, but also quantifies the aleatoric uncertainty inherent in the data. During the training process, the CNN is reformulated using Bayesian principles, and the Bayes-by-backprop method is applied to train the network. This transformation converts model parameters from fixed values into random variables. As a result, epistemic uncertainty caused by model inaccuracies and limited data can be quantified. Experimental validation on the IEEE PHM Challenge 2012 dataset demonstrated that the proposed method achieved a higher prediction accuracy than state-of-the-art uncertainty-aware prediction approaches, demonstrating a better applicability in engineering practice. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

20 pages, 2991 KB

Open AccessArticle

Advancing Defect Detection in Laser Welding: A Machine Learning Approach Based on Spatter Feature Analysis

by Gleb Solovev, Evgenii Klokov, Dmitrii Krasnov and Mikhail Sokolov

Sensors 2026, 26(6), 1825; https://doi.org/10.3390/s26061825 - 13 Mar 2026

Viewed by 597

Abstract

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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15 pages, 3575 KB

Open AccessArticle

Production System Monitoring Based on Petri Nets Enhanced with Multi-Source Information

by Peng Liu, Xinze Li, Chenlong Zhang, Yanru Kang, Jun Qian and Weizheng Chen

Sensors 2026, 26(6), 1785; https://doi.org/10.3390/s26061785 - 12 Mar 2026

Viewed by 360

Abstract

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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30 pages, 34205 KB

Open AccessArticle

Defect-Intent Ambiguity Addressing for Training-Free Deterministic PCB Defect Localization via Template Selection and Dissimilarity Mapping

by Saiyan Saiyod, Woottichai Nonsakhoo, Zhengping Li and Piyanat Sirisawat

Sensors 2026, 26(5), 1541; https://doi.org/10.3390/s26051541 - 28 Feb 2026

Viewed by 408

Abstract

Automated optical inspection (AOI) for printed circuit boards (PCBs) requires localizing small, sparse defects under illumination drift and minor placement misalignment, while supporting fast, auditable pass/fail decisions. This paper presents a training-free, reference-based digital image processing framework with no learning/training stage that compares each defective query image with a small library of defect-free reference templates (for the same PCB layout/revision) using a small set of interpretable control parameters. A reference is selected by coarse-to-fine matching (fast pre-screening followed by SSIM refinement on a central region), and an optional global alignment is applied only when it increases SSIM to limit defect-driven over-correction. Defects are highlighted by a defect-likelihood field that fuses an SSIM-derived structural dissimilarity map with a normalized absolute-difference map, followed by connected-component extraction to produce confidence-ranked bounding boxes. The method achieves Precision = 0.9663, Recall = 0.9987, and F1 = 0.9822 at the best-F1 operating point (0.149 false positives per image). Under the adopted box-matching protocol, average precision reaches 0.984. Precision–recall and FROC curves are reported to support threshold selection under different false-alarm budgets. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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19 pages, 2606 KB

Open AccessArticle

Composite Fault Feature Index-Guided Variational Mode Decomposition with Dynamic Weighted Central Clustering for Bearing Fault Detection

by Bangcheng Zhang, Boyu Shen, Zhi Gao, Yubo Shao, Zaixiang Pang and Xiaojing Yin

Sensors 2026, 26(4), 1394; https://doi.org/10.3390/s26041394 - 23 Feb 2026

Viewed by 490

Abstract

To address the periodic impacts and amplitude-modulated high-frequency resonance phenomena caused by bearing faults in rotating machinery, this paper proposes a detection method. The core innovation lies in: firstly, constructing a composite fault feature index (CFFI) that integrates normalized kurtosis and fuzzy entropy, which synchronously quantifies the fault impact intensity and periodic structure, and serves as an optimization objective; secondly, definining a spectral energy retention rate (SERR) that includes both the full spectrum and characteristic frequency bands to evaluate the denoising effect and fault feature retention, respectively. Based on this, the method adaptively determines the Variational Mode Decomposition (VMD) parameters through the Triangular Topology Aggregation Optimizer (TTAO), and uses Dynamic Weighted Center Clustering (DWCC) to screen key IMFs containing fault-envelope information. On the IMS bearing dataset, the SERR of the reconstructed signal is 0.21356, which is higher than the actual collected signal value of 0.22465, with a relative error of 4.9%, indicating a higher reconstruction accuracy. These quantitative results indicate that CFFI-guided optimization enhances impulsive and periodic fault components while maintaining stable feature-band retention. This approach is suitable for real-world equipment monitoring and exhibits strong engineering applicability. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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13 pages, 1565 KB

Open AccessArticle

Correlation Between Meso-Defect and Fatigue Life Through Representing Feature Analysis for 6061-T6 Aluminum Alloys

by Liangxia Zhang, Yali Yang, Hao Chen and Shusheng Lv

Sensors 2026, 26(2), 631; https://doi.org/10.3390/s26020631 - 17 Jan 2026

Viewed by 337

Abstract

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged fatigue damage, meso-defect data was obtained by X-ray CT. After 3D reconstruction and simplification, porosity, shape, and location were selected as the meso-defect-representing features using correlation coefficient analysis. Weights of meso-defect features were determined through FEM simulation. A mesoscopic damage variable incorporating the weights of porosity, shape, and location for meso-defect was defined. Correlation between fatigue life and meso-defect features was established through the mesoscopic damage variable. Experimental verification results showed that the prediction method is an effective method for fatigue life assessment. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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26 pages, 8544 KB

Open AccessArticle

Hi-MDTCN: Hierarchical Multi-Scale Dilated Temporal Convolutional Network for Tool Condition Monitoring

by Anying Chai, Zhaobo Fang, Mengjia Lian, Ping Huang, Chenyang Guo, Wanda Yin, Lei Wang, Enqiu He and Siwen Li

Sensors 2025, 25(24), 7603; https://doi.org/10.3390/s25247603 - 15 Dec 2025

Viewed by 830

Abstract

Accurate identification of tool wear conditions is of great significance for extending tool life, ensuring processing quality, and improving production efficiency. Current research shows that signals collected by a single sensor have limited dimensions and cannot comprehensively capture the degradation process of tool wear, while multi-sensor fusion recognition methods cannot effectively handle the complementarity and redundancy between heterogeneous sensor data in feature extraction and fusion. To address these issues, this paper proposes Hi-MDTCN (Hierarchical Multi-scale Dilated Temporal Convolutional Network). In the network, we propose a hierarchical signal analysis framework that processes the signal in segments. When processing intra-segment signals, we design a Multi-channel one-dimensional convolutional network with attention mechanism to capture local wear features at different time scales and fuse them into a unified representation. When processing signal segments, we design a Bi-TCN module to further capture long-term dependencies in wear evolution, mining the overall trend of tool wear over time. Hi-MDTCN adopts a dilated convolution mechanism, which can achieve an extremely large receptive field without building an overly deep network structure, effectively solving problems faced by recurrent neural networks in long sequence modeling such as gradient vanishing, low training efficiency, and poor parallel computing capability, achieving efficient parallel capture of long-range dependencies in time series. Finally, the proposed method is applied to the PHM2010 milling data. Experimental results show that the model’s tool condition recognition accuracy is higher than traditional methods, demonstrating its effectiveness for practical applications. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

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