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Sensors for Machine Condition Monitoring and Fault Detection
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Sensors for Machine Condition Monitoring and Fault Detection

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 10244

Special Issue Editors

Dr. Wu-sung Yao

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, No. 1 University Rd., Yanchao District, Kaohsiung City 824, Taiwan
Interests: servo control; power-assisted system; mechatronics; energy-saving management
Dr. Powen Hsueh

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, No. 1 University Rd., Yanchao District, Kaohsiung City 824, Taiwan
Interests: hybrid systems; nonlinear systems; adaptive control; switching control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Approximately half of all operating costs in most processing and manufacturing operations can be attributed to maintenance. This is ample motivation for studying any activity that can potentially lower these costs. Machine condition monitoring and fault diagnostics is one of these activities. Machine condition monitoring and fault diagnostics can be defined as the field of technical activity in which selected physical parameters, associated with machinery operation, are observed for the purpose of determining machinery integrity. In recent years, the integration of sensors monitoring different magnitudes to develop fault diagnosis and monitoring technologies of machines has attracted increasing attention from both academia and industry.

We invite researchers from both academia and industry to submit original and unpublished manuscripts to this Special Issue to showcase some of the recent developments within these topics.

The goal of the Special Issue is to publish the most recent research results and industrial applications in Sensors for Machine Condition Monitoring and Fault Detection. Topics that are suitable for this Special Issue include, but are not limited to:

  • Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis;
  • Trend of condition monitoring and fault diagnosis of multi-sensors information fusion;
  • Computational intelligence techniques for machinery condition monitoring and fault diagnosis;
  • Sensors in advanced fault diagnosis and monitoring applications in different industrial application;
  • Methods, concepts, and performance assessment for improving the fault diagnosis of existing techniques in the field of machines;
  • Integrated condition monitoring and fault diagnosis for modern manufacturing systems;
  • Artificial intelligence application in machine condition monitoring and fault diagnosis;
  • Review of condition monitoring and fault diagnosis technologies;

Dr. Wu-sung Yao
Dr. Powen Hsueh
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.

Published Papers (4 papers)

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Research

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19 pages, 3261 KiB  
Article
A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction
by Zhengyang Fan, Wanru Li and Kuo-Chu Chang
Sensors 2024, 24(3), 824; https://doi.org/10.3390/s24030824 - 26 Jan 2024
Cited by 1 | Viewed by 1100
Abstract
The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application [...] Read more.
The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction. Full article
(This article belongs to the Special Issue Sensors for Machine Condition Monitoring and Fault Detection)
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22 pages, 7745 KiB  
Article
Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
by Iulian Lupea and Mihaiela Lupea
Sensors 2023, 23(21), 8769; https://doi.org/10.3390/s23218769 - 27 Oct 2023
Cited by 3 | Viewed by 1338
Abstract
A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, [...] Read more.
A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%. Full article
(This article belongs to the Special Issue Sensors for Machine Condition Monitoring and Fault Detection)
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16 pages, 4197 KiB  
Article
Compensation for Vanadium Oxide Temperature with Stereo Vision on Long-Wave Infrared Light Measurement
by Chun-Yi Lin and Wu-Sung Yao
Sensors 2022, 22(21), 8302; https://doi.org/10.3390/s22218302 - 29 Oct 2022
Viewed by 1377
Abstract
In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal [...] Read more.
In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal imager is with the long-wave infrared measurement. Based on Planck’s black body radiation law and Stefan–Boltzmann law, the binocular stereo calibration of the two cameras was calculated. In order to improve the measured temperature error at different distances, equipped with Intel Real Sense Depth Camera D435, a compensator is proposed to ensure that the measured temperature of the heat source is correct and accurate. From the results, it can be clearly seen that the actual measured temperature at each distance is proportional to the temperature of the thermal image vanadium oxide, while the actual measured temperature is inversely proportional to the distance of the test object. By the proposed compensation function, the compensation temperature at varying vanadium oxide temperatures can be obtained. The errors between the average temperature at each distance and the constant temperature of the test object at 39 °C are all less than 0.1%. Full article
(This article belongs to the Special Issue Sensors for Machine Condition Monitoring and Fault Detection)
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Review

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33 pages, 3937 KiB  
Review
A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models
by Fahad Alharbi, Suhuai Luo, Hongyu Zhang, Kamran Shaukat, Guang Yang, Craig A. Wheeler and Zhiyong Chen
Sensors 2023, 23(4), 1902; https://doi.org/10.3390/s23041902 - 8 Feb 2023
Cited by 23 | Viewed by 5431
Abstract
Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of [...] Read more.
Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations. The research community has begun using machine learning (ML) to detect idler’s defects to assist industries in responding to failures on time. Vibration and acoustic measurements are commonly employed to monitor the condition of idlers. However, there has been no comprehensive review of FD for belt conveyor idlers. This paper presents a recent review of acoustic and vibration signal-based fault detection for belt conveyor idlers using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models. Finally, it highlights critical open challenges and provides future research directions. Full article
(This article belongs to the Special Issue Sensors for Machine Condition Monitoring and Fault Detection)
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