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Artificial Intelligence Enhanced Health Monitoring and Diagnostics
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Artificial Intelligence Enhanced Health Monitoring and Diagnostics

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

Deadline for manuscript submissions: closed (25 February 2023) | Viewed by 22763

Special Issue Editors

Prof. Dr. Jun Wu

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: structure health monitoring; online fault monitoring and diagnosis; maintenance decision
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaojun Steven Li

E-Mail Website
Guest Editor
Department of Industrial Engineering and Engineering Management, Western New England University, Springfield, MA 01119, USA
Interests: quality and reliability engineering; prognostics and health management; predictive modeling; applied operations research and statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Qin

E-Mail Website
Guest Editor
1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
2. The State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China
Interests: signal processing; fault feature extraction; fault prognosis; life prediction; fault transfer diagnosis; vision measurement; digital twin; energy harvesting for sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Carman K.M. Lee

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: predictive maintenance; system reliability; industrial IoT; cyber physical system; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following people’s awareness of the importance of the reliabilty, safety and maintainability of industrial systems for a long period of time, innovative technologies for health monitoring and diagnostics of industrial systems have attracted increasing attention. In particular, with the rapid advances of artificial intelligence, intelligent Internet of Things (IoT) and industrial big data technologies, there have been increasing interests in the development of advanced artificial intelligence algorithms in order to address the challenges in the fields of condition monitoring, anomaly detection, fault prognostics and diagnostics of various industrial systems. Recently, diverse kinds of artificial intelligence algorithms, such as convolution neural network, adversarial adaptation network and extreme learning machine, have been developed for health monitoring and diagnostics in the light of massive monitoring data collected by sensors and IoT devices.

The aim of this Special Issue is to provide a platform for scientists, engineers and industrial practitioners to present their latest theoretical and technological advancements in artificial intelligence-enhanced health monitoring and diagnositics for industrial systems. High-quality research articles, short communication and reviews are welcome. Research studies that seek to address recent developments in advanced artificial intelligence algorithms are of special interest, such as deep learning, ensemble learning, transfer learning and reinforcement learning, and are well suited for enhancing the health monitoring, diagnositics and prognostics of industrial systems.

Papers are solicited in but are not limited to the following and related topics:

  • Artificial intelligence algorithms for health monitoring and fault diagnosis;
  • Big data mining methods for anomaly detection;
  • Deep learning methods for intelligent fault prognostics;
  • Deep transfer learning algorithms for fault diagnosis with small fault samples;
  • Deep reinforcement learning methods for prognostics and health management;
  • Artificial intelligence-based edge and cloud computing for health monitoring.

Prof. Dr. Jun Wu
Dr. Zhaojun Steven Li
Prof. Dr. Yi Qin
Dr. Carman K.M. Lee
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

  • artificial intelligence
  • deep learning
  • health monitoring
  • anomaly detection
  • fault diagnosis

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Related Special Issue

Published Papers (8 papers)

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Research

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23 pages, 7789 KiB  
Article
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, Georgia Karapostoli, Markus Seidel, Rosamaria Venditti, Luka Lambrecht, Emanuele Usai, Muhammad Ahmad, Javier Fernandez Menendez, Kaori Maeshima and the CMS-HCAL Collaboration
Sensors 2023, 23(24), 9679; https://doi.org/10.3390/s23249679 - 7 Dec 2023
Cited by 4 | Viewed by 2032
Abstract
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality [...] Read more.
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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19 pages, 2954 KiB  
Article
Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints
by Christian Goetz and Bernhard Humm
Sensors 2023, 23(9), 4207; https://doi.org/10.3390/s23094207 - 23 Apr 2023
Cited by 7 | Viewed by 2920
Abstract
Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the [...] Read more.
Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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16 pages, 10853 KiB  
Article
Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification
by Luigi Gianpio Di Maggio
Sensors 2023, 23(1), 211; https://doi.org/10.3390/s23010211 - 25 Dec 2022
Cited by 14 | Viewed by 3076
Abstract
The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge amount of data, which is scarcely available in industry. This work shows that convolutional networks pre-trained for audio classification already contain knowledge for classifying bearing vibrations, since both tasks share [...] Read more.
The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge amount of data, which is scarcely available in industry. This work shows that convolutional networks pre-trained for audio classification already contain knowledge for classifying bearing vibrations, since both tasks share the need to extract features from spectrograms. Knowledge transfer is realized through transfer learning to identify localized defects in rolling element bearings. This technique provides a tool to transfer the knowledge embedded in neural networks pre-trained for fulfilling similar tasks to diagnostic scenarios, significantly limiting the amount of data needed for fine-tuning. The VGGish model was fine-tuned for the specific diagnostic task by handling vibration samples. Data were extracted from the test bench for medium-size bearings specially set up in the mechanical engineering laboratories of the Politecnico di Torino. The experiment involved three damage classes. Results show that the model pre-trained using sound spectrograms can be successfully employed for classifying the bearing state through vibration spectrograms. The effectiveness of the model is assessed through comparisons with the existing literature. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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21 pages, 5283 KiB  
Article
Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine
by Hao Yan, Huajun Bai, Xianbiao Zhan, Zhenghao Wu, Liang Wen and Xisheng Jia
Sensors 2022, 22(21), 8325; https://doi.org/10.3390/s22218325 - 30 Oct 2022
Cited by 22 | Viewed by 2372
Abstract
Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on [...] Read more.
Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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14 pages, 3214 KiB  
Article
Wide Residual Relation Network-Based Intelligent Fault Diagnosis of Rotating Machines with Small Samples
by Zuoyi Chen, Yuanhang Wang, Jun Wu, Chao Deng and Weixiong Jiang
Sensors 2022, 22(11), 4161; https://doi.org/10.3390/s22114161 - 30 May 2022
Cited by 9 | Viewed by 2045
Abstract
Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus [...] Read more.
Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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18 pages, 2138 KiB  
Article
Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions
by Huatong Dai, Pengzhan Chen and Hui Yang
Sensors 2022, 22(3), 845; https://doi.org/10.3390/s22030845 - 22 Jan 2022
Cited by 8 | Viewed by 2643
Abstract
Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based [...] Read more.
Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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19 pages, 7225 KiB  
Article
Global Wave Velocity Change Measurement of Rock Material by Full-Waveform Correlation
by Jing Zhou, Zilong Zhou, Yuan Zhao and Xin Cai
Sensors 2021, 21(22), 7429; https://doi.org/10.3390/s21227429 - 9 Nov 2021
Cited by 2 | Viewed by 2153
Abstract
Measuring accurate wave velocity change is a crucial step in damage assessment of building materials such as rock and concrete. The anisotropy caused by the generation of cracks in the damage process and the uncertainty of the damage level of these building materials [...] Read more.
Measuring accurate wave velocity change is a crucial step in damage assessment of building materials such as rock and concrete. The anisotropy caused by the generation of cracks in the damage process and the uncertainty of the damage level of these building materials make it difficult to obtain accurate wave velocity change. We propose a new method to measure the wave velocity change of anisotropic media at any damage level by full-waveform correlation. In this method, the anisotropy caused by the generation of cracks in the damage process is considered. The accuracy of the improved method is verified by numerical simulation and compared with the existing methods. Finally, the proposed method is applied to measure the wave velocity change in the damage process of rock under uniaxial compression. We monitor the failure process of rock by acoustic emission (AE) monitoring system. Compared with the AE ringing count, the result of damage evaluation obtained by the proposed method is more accurate than the other two methods in the stage of increasing rock heterogeneity. These results show that the proposed method is feasible in damage assessment of building materials such as rock and concrete. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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Review

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29 pages, 2357 KiB  
Review
Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review
by Arturas Kairys, Renata Pauliukiene, Vidas Raudonis and Jonas Ceponis
Sensors 2023, 23(7), 3618; https://doi.org/10.3390/s23073618 - 30 Mar 2023
Cited by 8 | Viewed by 3757
Abstract
It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection [...] Read more.
It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research. Full article
(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics)
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