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Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 9072

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Special Issue Editor

Dr. Md Junayed Hasan

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Guest Editor

National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK
Interests: digital condition monitoring; mechanical signal processing; computer vision, machine learning; multimodal data fusion; artificial intelligence; digital fault inspection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the industrial sector, machinery and mechanical structures are susceptible to deterioration and performance decline over time. Consequently, the collection and processing of data from a variety of sensors has become crucial for the timely diagnosis of deterioration symptoms and the accurate prediction of future health conditions. Using artificial intelligence (AI) technology, models are being developed based on historical sensor data which have enormous potential for fault diagnosis and prognosis in industrial equipment. As the deployment of Internet of Things (IoT) and cloud-based technologies for stateful maintenance increases in the future, AI-powered solutions will become even more crucial for managing the vast quantities of available measurement data for decision making.

This Special Issue aims to investigate fault diagnosis and prognosis of industrial equipment and mechanical structures by utilising a variety of sensors, including those related to image, video, and multimodal information fusion. We welcome researchers to submit articles discussing sensor-based artificial neural network technology, multimodal data fusion, explainable AI solutions, and objects for error diagnosis and prognosis in the context of Industry 4.0, cloud computing, cyber–physical systems, and machine-to-machine interfaces and paradigms.

Dr. Md Junayed Hasan
Guest Editor

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Keywords

multimodal data fusion
fault diagnosis
digital condition monitoring
predictive maintenance
artificial intelligence
net-zero challenges with AI

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Published Papers (6 papers)

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Research

22 pages, 5157 KiB

Open AccessArticle

Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single Exponential Degradation Model

by Lorenzo Ciani, Cristian Garzon-Alfonso, Francesco Grasso and Gabriele Patrizi

Sensors 2025, 25(7), 2275; https://doi.org/10.3390/s25072275 - 3 Apr 2025

Viewed by 216

Abstract

One of the most critical items from the reliability and the State-of-Health (SOH) point of view of wireless sensor networks is represented by lithium batteries. Predicting the SOH of batteries in sensor-equipped smart grids is crucial for optimizing energy management, preventing failures, and extending battery lifespan. Accurate SOH estimation enhances grid reliability, reduces maintenance costs, and facilitates the efficient integration of renewable energy sources. In this article, a solution for SOH prediction and the estimation of the Remaining Useful Life (RUL) of lithium batteries is presented. The approach was implemented and tested using two training datasets: the first consists of raw data provided by the Prognostics Center of Excellence at NASA, comprising 168 records, while the second is based on the curve fitting of the measured data using a single exponential degradation model. Long Short-Term Memory networks (LSTMs) were trained using data from three different scenarios, where battery cycle consumption reached 30%, 50%, and 65% correspondingly. Various architectures and hyperparameters were explored to optimize the models’ performance. The key finding is that training one of the models with only 50 records (equivalent to 30% of battery usage) enables accurate SOH prediction, achieving a Mean Squared Error (MSE) of

1.68 \times 10^{- 4}

and Root Mean Squared Error (RMSE) of

1.30 \times 10^{- 2}

. The best model trained with 110 records achieved an MSE of

2.51 \times 10^{- 5}

and an RMSE of

5.01 \times 10^{- 3}

. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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20 pages, 8443 KiB

Open AccessArticle

Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models

by Josef Koutsoupakis, Dimitrios Giagopoulos, Panagiotis Seventekidis, Georgios Karyofyllas and Amalia Giannakoula

Sensors 2025, 25(1), 101; https://doi.org/10.3390/s25010101 - 27 Dec 2024

Viewed by 756

Abstract

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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24 pages, 19686 KiB

Open AccessArticle

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly

by Hong-Dar Lin, Cheng-Kai Jheng, Chou-Hsien Lin and Hung-Tso Chang

Sensors 2024, 24(11), 3635; https://doi.org/10.3390/s24113635 - 4 Jun 2024

Cited by 1 | Viewed by 1652

Abstract

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product’s normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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19 pages, 7702 KiB

Open AccessArticle

Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold

by Baojun Yang, Wei Liu, Sheng Lu and Jiufei Luo

Sensors 2024, 24(5), 1380; https://doi.org/10.3390/s24051380 - 21 Feb 2024

Cited by 2 | Viewed by 1193

Abstract

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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30 pages, 3401 KiB

Open AccessArticle

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors

by Yanyang Li, Jindong Wang, Haiyang Zhao, Chang Wang and Qi Shao

Sensors 2024, 24(1), 167; https://doi.org/10.3390/s24010167 - 27 Dec 2023

Cited by 3 | Viewed by 1824

Abstract

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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18 pages, 6557 KiB

Open AccessArticle

Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5

by Shengsuo Niu, Xiaosen Zhou, Dasen Zhou, Zhiyao Yang, Haiping Liang and Haifeng Su

Sensors 2023, 23(14), 6410; https://doi.org/10.3390/s23146410 - 14 Jul 2023

Cited by 9 | Viewed by 2511

Abstract

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a “morphological experiment”, while an “algorithm comparison experiment” confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Fault Diagnosis and Condition Monitoring)

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