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Reliability Verification and Diagnosis Methods for Mechanical Equipment
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Reliability Verification and Diagnosis Methods for Mechanical Equipment

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 6751

Special Issue Editors

Dr. Haiyang Pan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: equipment status monitoring and fault diagnosis; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Li

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: artificial-intelligence-based testing and verification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xinhua Liu

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligence manufacturing and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reliability Verification and Diagnosis Methods for Mechanical Equipment has greatly contributed to ensuring the dependability and safety of modern technologies. some notable developments are achieved in this field, such as formal verification, model-based testing, property-based testing, fault detection/diagnosis, continuous integration and testing, machine learning in testing. These reliability verification and diagnosis methods play a crucial role in ensuring the reliability and safety of systems in various domains, including automotive, manufacturing, aerospace, telecommunications, and critical infrastructure. The development and application of sensor technology is critical in this field.

The purpose of this subject is to promote the reliability and dependability of reliable systems, and propose a variety of novel high-quality verification and test methods. We welcome both original research articles and review articles discussing the current state of the art. Research areas may include (but not limited to) the following:

  1. Advances in formal verification methods.
  2. Novel model-based testing and verification techniques.
  3. Data or model-driven fault detection and diagnosis methods.
  4. Machine Learning in testing and verification approaches.
  5. Cutting-edge hardware verification techniques.
  6. Continuous integration and testing

We look forward to receiving your contributions.

Dr. Haiyang Pan
Dr. Xin Li
Prof. Dr. Xinhua Liu
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

  • machine learning
  • signal processing
  • fault diagnosis
  • verification technique
  • feature extraction
  • sensor technology

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Published Papers (5 papers)

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Research

21 pages, 10495 KiB  
Article
MR-FuSN: A Multi-Resolution Selective Fusion Approach for Bearing Fault Diagnosis
by Lin Sha, Shikai Tang, Min Wang, Sibo Qiao, Shihang Yu, Weixia Liu and Jiaqi Li
Sensors 2025, 25(4), 1134; https://doi.org/10.3390/s25041134 - 13 Feb 2025
Viewed by 457
Abstract
Vibration signals serve as the primary data source for bearing fault diagnosis. However, when collected in complex industrial environments, these signals are often contaminated by noise interference, posing significant challenges to fault feature extraction and diagnostic accuracy. To address these issues, this paper [...] Read more.
Vibration signals serve as the primary data source for bearing fault diagnosis. However, when collected in complex industrial environments, these signals are often contaminated by noise interference, posing significant challenges to fault feature extraction and diagnostic accuracy. To address these issues, this paper proposes a novel bearing fault diagnosis network architecture: the Multi-Resolution Fusion Selection Network (MR-FuSN). The MR-FuSN effectively extracts domain-invariant features from input data through multi-resolution feature extraction and incorporates an adaptive kernel convolution strategy, thereby enhancing its robustness against environmental noise. Experimental results demonstrate that the MR-FuSN achieves outstanding performance in noisy environments with signal-to-noise ratios (SNRs) ranging from −5 dB to 10 dB, particularly attaining a diagnostic accuracy of 99.97% under 0 dB conditions. This study provides technical support for practical fault diagnosis applications. Full article
(This article belongs to the Special Issue Reliability Verification and Diagnosis Methods for Mechanical Equipment)
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14 pages, 5327 KiB  
Article
Ensemble All Time-Scale Decomposition Method and Its Application in Bevel Gear Fault Diagnosis
by Zhengyang Cheng, Yu Yang, Chengcheng Duan, Xin Kang and Jianxin Cui
Sensors 2025, 25(1), 23; https://doi.org/10.3390/s25010023 - 24 Dec 2024
Viewed by 674
Abstract
All time-scale decomposition (ATD) is a non-parametric adaptive signal decomposition method, which relies on zero-crossing points and extreme points to jointly construct the baseline, achieving the suppression of modal mixing caused by the proximity of component frequencies. However, ATD is unable to solve [...] Read more.
All time-scale decomposition (ATD) is a non-parametric adaptive signal decomposition method, which relies on zero-crossing points and extreme points to jointly construct the baseline, achieving the suppression of modal mixing caused by the proximity of component frequencies. However, ATD is unable to solve mode mixing induced by noise. To improve this defect, a new noise-assisted signal decomposition method named ensemble all time-scale decomposition (EATD) is proposed in this paper. EATD introduces the noise-assisted technique of complementary ensemble empirical mode decomposition based on ATD, adding complementary noises to mask the noise interference in the signal. EATD not only overcomes mode mixing caused by noise but also preserves the capability of ATD to suppress mode mixing caused by the proximity of component frequencies. Simulation signals and bevel gear fault signals are utilized to validate EATD, and the results indicate that EATD can successfully overcome mode mixing induced by noise and can be effectively applied for gear fault diagnosis. Full article
(This article belongs to the Special Issue Reliability Verification and Diagnosis Methods for Mechanical Equipment)
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17 pages, 6764 KiB  
Article
Fault Diagnosis Method for Vacuum Contactor Based on Time-Frequency Graph Optimization Technique and ShuffleNetV2
by Haiying Li, Qinyang Wang and Jiancheng Song
Sensors 2024, 24(19), 6274; https://doi.org/10.3390/s24196274 - 27 Sep 2024
Viewed by 819
Abstract
This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels [...] Read more.
This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor λ. The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%. Full article
(This article belongs to the Special Issue Reliability Verification and Diagnosis Methods for Mechanical Equipment)
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25 pages, 5632 KiB  
Article
Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands
by Iulian Lupea, Mihaiela Lupea and Adrian Coroian
Sensors 2024, 24(11), 3337; https://doi.org/10.3390/s24113337 - 23 May 2024
Cited by 5 | Viewed by 1768
Abstract
The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities [...] Read more.
The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels. Full article
(This article belongs to the Special Issue Reliability Verification and Diagnosis Methods for Mechanical Equipment)
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17 pages, 5578 KiB  
Article
Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis
by Saif Ullah, Zahoor Ahmad and Jong-Myon Kim
Sensors 2024, 24(6), 1830; https://doi.org/10.3390/s24061830 - 13 Mar 2024
Cited by 7 | Viewed by 1837
Abstract
This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band [...] Read more.
This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time–frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification. Full article
(This article belongs to the Special Issue Reliability Verification and Diagnosis Methods for Mechanical Equipment)
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