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Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 7162

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Special Issue Editors

Dr. Tianyu Gao

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Guest Editor

Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China
Interests: fault diagnosis; condition monitoring; machine learning; deep learning; interpretability

Dr. Yinsheng Chen

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Guest Editor

School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Interests: machine olfaction; electronic noise; sensor fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Jianyu Wang

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Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu, China
Interests: anomaly detection; fault diagnosis; deep learning; transfer learning; electromechanical equipment

Dr. Xiaoli Zhao

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Guest Editor

School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
Interests: fault diagnosis; health management

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the advancements and applications of intelligent sensors for condition monitoring, diagnosis, and prognostics. We seek original research papers that showcase the latest sensor technologies and intelligent algorithms aimed at enhancing real-time monitoring, precise diagnostics, and accurate prognosis of machine health. Contributions should focus on the exploration on the integration of advanced analytics, including AI and ML, with intelligent sensors to improve the accuracy and speed of data processing and decision-making.

We are particularly interested in papers that demonstrate how intelligent sensors enable real-time decision-making and improve operational policies. Research addressing both electro-mechanical systems (e.g., rotational machinery) and emerging systems (e.g., renewable energy) is encouraged. Moreover, papers that showcase the application of intelligent sensors in diverse industries and contexts, including but not limited to manufacturing, transportation, healthcare, and environmental monitoring, are also encouraged.

Overall, we aim to bring together a collection of high-quality research papers that exhibit the latest advancements and applications of intelligent sensors in condition monitoring, diagnosis, and prognostics, and their potential to revolutionize various industries and improve our daily lives.

Dr. Tianyu Gao
Dr. Yinsheng Chen
Dr. Jianyu Wang
Dr. Xiaoli Zhao
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

intelligent sensors
measurement
condition monitoring
anomaly detection
fault diagnosis
prognostics
machine learning
deep learning
signal processing

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Published Papers (9 papers)

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Research

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32 pages, 3638 KiB

Open AccessArticle

Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing

by Suchuan Xing, Yihan Wang and Wenhe Liu

Sensors 2025, 25(11), 3396; https://doi.org/10.3390/s25113396 - 28 May 2025

Viewed by 186

Abstract

Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. Our approach employs contrastive learning to create unified representations of diverse service metrics and incorporates causal inference mechanisms to trace fault propagation paths. We evaluated our framework using a semi-supervised learning approach that leveraged both labeled anomalies and abundant normal data, achieving 95.4% detection accuracy, 93.8% F1-score, and 87.6% precision in fault component localization. The system reduced the average troubleshooting time by 43% and false localization rates by 31% compared to state-of-the-art methods, while maintaining a computational efficiency suitable for real-time monitoring. These results demonstrate the effectiveness of our approach in identifying and precisely localizing anomalies in complex microservice environments through intelligent sensing of system metrics, enabling proactive maintenance strategies that minimize service disruptions. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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12 pages, 1630 KiB

Open AccessArticle

Reinforcing Deep Learning-Enabled Surveillance with Smart Sensors

by Taewoo Lee, Yumin Choi and Hyunbum Kim

Sensors 2025, 25(11), 3345; https://doi.org/10.3390/s25113345 - 26 May 2025

Viewed by 182

Abstract

It is critical to solidify surveillance in 3D environments with heterogeneous sensors. This study introduces an innovative deep learning-assisted surveillance reinforcement system with smart sensors for resource-constrained cyber-physical devices and mobile elements. The proposed system incorporates deep learning technologies to address the challenges of dynamic public environments. By enhancing the adaptability and effectiveness of surveillance in environments with high human mobility, this paper aims to optimize surveillance node placement and ensure real-time system responsiveness. The integration of deep learning not only improves accuracy and efficiency but also introduces unprecedented flexibility in surveillance operations. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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28 pages, 4628 KiB

Open AccessArticle

Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems

by Joung-Hwan Yoon, Chibuzo Nwabufo Okwuosa, Nnamdi Chukwunweike Aronwora and Jang-Wook Hur

Sensors 2025, 25(8), 2449; https://doi.org/10.3390/s25082449 - 13 Apr 2025

Viewed by 437

Abstract

The industrial application of artificial intelligence (AI) has witnessed outstanding adoption due to its robust efficiency in recent times. Image fault detection and classification have also been implemented industrially for product defect detection, as well as for maintaining standards and optimizing processes using AI. However, there are deep concerns regarding the latency in the performance of AI for fault detection in glossy and curved surface products, due to their nature and reflective surfaces, which hinder the adequate capturing of defective areas using traditional cameras. Consequently, this study presents an enhanced method for curvy and glossy surface image data collection using a Basler vision camera with specialized lighting and KEYENCE displacement sensors, which are used to train deep learning models. Our approach employed image data generated from normal and two defect conditions to train eight deep learning algorithms: four custom convolutional neural networks (CNNs), two variations of VGG-16, and two variations of ResNet-50. The objective was to develop a computationally robust and efficient model by deploying global assessment metrics as evaluation criteria. Our results indicate that a variation of ResNet-50, ResNet-50₂₂₄, demonstrated the best overall efficiency, achieving an accuracy of 97.97%, a loss of 0.1030, and an average training step time of 839 milliseconds. However, in terms of computational efficiency, it was outperformed by one of the custom CNN models, CNN₆-240, which achieved an accuracy of 95.08%, a loss of 0.2753, and an average step time of 94 milliseconds, making CNN₆-240 a viable option for computational resource-sensitive environments. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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22 pages, 1021 KiB

Open AccessArticle

State-Based Fault Diagnosis of Finite-State Vector Discrete-Event Systems via Integer Linear Programming

by Qinrui Chen, Mubariz Garayev and Ding Liu

Sensors 2025, 25(5), 1452; https://doi.org/10.3390/s25051452 - 27 Feb 2025

Viewed by 410

Abstract

This paper presents a state-based method to address the verification of

K

This paper presents a state-based method to address the verification of

K

-diagnosability and fault diagnosis of a finite-state vector discrete-event system (Vector DES) with partially observable state outputs due to limited sensors. Vector DES models consist of an arithmetic additive structure in both the state space and state transition function. This work offers a necessary and sufficient condition for verifying the

K

-diagnosability of a finite-state Vector DES based on state sensor outputs, employing integer linear programming and the mathematical representation of a Vector DES. Predicates are employed to diagnose faults in a Vector DES online. Specifically, we use three different kinds of predicates to divide system state outputs into different subsets, and the fault occurrence in a system is detected by checking a subset of outputs. Online diagnosis is achieved via solving integer linear programming problems. The conclusions obtained in this work are explained by means of several examples. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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21 pages, 3635 KiB

Open AccessArticle

Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance

by Qing Dong, Hong Pei, Changhua Hu, Jianfei Zheng and Dangbo Du

Sensors 2025, 25(4), 1218; https://doi.org/10.3390/s25041218 - 17 Feb 2025

Viewed by 618

Abstract

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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14 pages, 15017 KiB

Open AccessArticle

Maintenance Decision-Making Using Intelligent Prognostics Within a Single Spare Parts Support System

by Bowei Zhang, Changhua Hu, Jianfei Zheng and Hong Pei

Sensors 2025, 25(3), 837; https://doi.org/10.3390/s25030837 - 30 Jan 2025

Viewed by 800

Abstract

Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare parts ordering and replacement is proposed within a spare parts support system. The decision-making process aims to minimize costs while maximizing availability as its primary objective. It considers spare parts ordering time and replacement time as key decision variables. By developing a maintenance decision-making model, it aims to determine the optimal time for ordering and replacing spare parts. This maintenance approach is designed to provide technical support for effective and rational equipment management decision-making. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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26 pages, 13903 KiB

Open AccessArticle

Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis

by Hao Shen, Yufan Lv, Yun Kong, Qinkai Han, Ke Chen, Zhibo Geng, Mingming Dong and Fulei Chu

Sensors 2024, 24(23), 7618; https://doi.org/10.3390/s24237618 - 28 Nov 2024

Viewed by 826

Abstract

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg⁻¹·rpm⁻¹. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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12 pages, 3089 KiB

Open AccessArticle

Effect of Dielectric Layer on Miniaturized Patch Antenna Sensor

by Caifeng Chen, Lei Zou, Chenglong Bi and Andong Wang

Sensors 2024, 24(23), 7608; https://doi.org/10.3390/s24237608 - 28 Nov 2024

Cited by 1 | Viewed by 1271

Abstract

Miniature patch antenna sensors have great potential in the field of structural health monitoring for crack propagation detection due to their small size and high sensitivity. A primary research focus has been achieving efficient miniaturization, with the performance of the dielectric layer playing a pivotal role. Studies have demonstrated that increasing the relative dielectric constant (ε_r) of the dielectric layer can reduce antenna size, but higher dielectric losses (tanδ) can lower radiation efficiency. This study identifies the optimal dielectric properties by examining the interplay between ε_r and tanδ to balance size reduction and radiation efficiency. Additionally, while increasing the dielectric layer’s thickness improves bandwidth and radiation efficiency, a thinner layer is preferred to maintain overall performance without compromising radiation efficiency. Furthermore, the resonant frequency of the smaller-sized patch antenna sensor exhibits greater detection sensitivity to crack propagation. These insights provide useful guidance for selecting effective dielectric layers and assist in the miniaturization design of antenna sensors. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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Review

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41 pages, 10236 KiB

Open AccessReview

Coaxial Cable Distributed Strain Sensing: Methods, Applications and Challenges

by Stephanie King, Gbanaibolou Jombo, Oluyomi Simpson, Wenbo Duan and Adrian Bowles

Sensors 2025, 25(3), 650; https://doi.org/10.3390/s25030650 - 22 Jan 2025

Viewed by 1363

Abstract

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)

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