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Processes
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Fault Diagnosis Technology in Machinery Manufacturing
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Fault Diagnosis Technology in Machinery Manufacturing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1343

Special Issue Editors

Prof. Dr. Grover Zurita Villarroel

E-Mail Website
Guest Editor
Lab. De Innovation Tecnologica Industrial y Robotica (LITIR), Universidad Privada Boliviana (UPB), Cochabamba, Bolivia, Sweden
Interests: vvibration analysis; machine diagnostic; artificial intelligence; modal analysis; digital signal processing; noise vibration hardness
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. René-Vinicio Sánchez

E-Mail Website
Guest Editor
Research and Development Group in Industrial Technologies (GIDTEC), Universidad Politecnica Salesiana de Cuenca. Av, de la Americas 20, Cuenca, Ecuador
Interests: machine diagnosis; machine diagnostics; noise emissions; artificial intelligence

Special Issue Information

Dear Colleagues,

Fault diagnosis in machinery manufacturing is a critical aspect that ensures the reliability, safety, and efficiency of industrial operations. In the age of Industry 4.0, production equipment is becoming more integrated and intelligent, introducing new challenges for data-driven process monitoring and fault diagnosis. This journal explores the current technologies and methodologies used in diagnosing faults in machinery. It highlights the integration of traditional techniques, such as vibration analysis and thermal imaging, with modern advancements like machine learning, artificial intelligence (AI), and the Internet of Things (IoT). These innovations enable real-time monitoring, predictive maintenance, and data-driven decision-making. This journal also integrates the challenges in implementing fault diagnosis systems, including data management, integration with existing systems, and the need for skilled personnel.

Through recent R&D advancements, insights have been provided into the future trends in fault diagnosis technologies, emphasizing the potential for increased automation and accuracy, as well as the development of smarter manufacturing processes.

Prof. Dr. Grover Zurita Villarroel
Prof. Dr. René-Vinicio Sánchez
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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • fault detection and diagnosis technology for machine manufacturing
  • vibration analysis for machine manufacturing
  • advances diagnostics techniques for machine manufacturing
  • neural networks methods for machine manufacturing
  • fault diagnosis methods for smart manufacturing
  • IoT-based monitoring and diagnostics of manufacturing systems
  • remote control and detection and detection technology for intelligent manufacturing
  • machine learning methods for machine manufacturing
  • applied artificial intelligence for fault detection and diagnosis technology for machine manufacturing
  • operational mode analysis for fault diagnostics and diagnostic for machine manufacturing

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Published Papers (3 papers)

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Research

19 pages, 4846 KiB  
Article
Research on the Degradation Model of a Smart Circuit Breaker Based on a Two-Stage Wiener Process
by Zhenhua Xie, Jianmin Ren, Puquan He, Linming Hou and Yao Wang
Processes 2025, 13(6), 1719; https://doi.org/10.3390/pr13061719 - 30 May 2025
Viewed by 374
Abstract
As the global energy transition moves towards the goal of low-carbon sustainability, it is crucial to build a new energy power system. The performance and reliability of Smart Circuit Breakers are the key to ensuring safe operation. The control circuit is the key [...] Read more.
As the global energy transition moves towards the goal of low-carbon sustainability, it is crucial to build a new energy power system. The performance and reliability of Smart Circuit Breakers are the key to ensuring safe operation. The control circuit is the key to the reliability of Smart Circuit Breakers, so studying its performance-degradation process is of great significance. This study centers on the development of a degradation model and the performance-degradation-assessment method for the control circuit of Smart Circuit Breakers and proposes a novel approach for lifetime prediction. Firstly, a test platform is established to collect necessary data for developing a performance-degradation model based on the two-stage Wiener process. According to the theory of maximum likelihood estimation and Schwarz information criterion, the estimation method of model distribution parameters in each degradation stage and the degradation ‘turning point’ method are studied. Then, reliability along with residual life serve as evaluation criteria for analyzing the control circuit’s performance deterioration. Taking the degradation characteristic data into the degradation model, for example, analysis, combined with the Arrhenius empirical formula, the reliability function at room temperature and the curve of the residual life probability density function is obtained. Ultimately, the average service life of the Smart Circuit Breaker control circuit at room temperature is 178,100 h (20.3 years), with a degradation turning point at 155,000 h (17.7 years), providing a basis for the lifetime evaluation of low-voltage circuit breakers. Full article
(This article belongs to the Special Issue Fault Diagnosis Technology in Machinery Manufacturing)
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18 pages, 3414 KiB  
Article
A Data-Driven Framework for Fault Diagnostics in Gearbox Health Monitoring Under Non-Stationary Conditions
by Nhan-Phuc Hoang, Trong-Du Nguyen, Tuan-Hung Nguyen, Duong-Hung Pham, Phong-Dien Nguyen and Thi-Van-Huong Nguyen
Processes 2025, 13(6), 1663; https://doi.org/10.3390/pr13061663 - 26 May 2025
Viewed by 321
Abstract
Monitoring gearbox health is essential in industrial systems, where undetected faults can result in costly downtime and severe equipment damage. While vibration-based diagnostics are widely utilized for fault detection, analyzing large-scale, non-stationary vibration signals remains a computational challenge, particularly in real-time and resource-constrained [...] Read more.
Monitoring gearbox health is essential in industrial systems, where undetected faults can result in costly downtime and severe equipment damage. While vibration-based diagnostics are widely utilized for fault detection, analyzing large-scale, non-stationary vibration signals remains a computational challenge, particularly in real-time and resource-constrained environments. This paper presents Data-Driven Synchrosqueezing-based Signal Transformation (DSST), a novel time-frequency method that integrates synchrosqueezing transform (SST) with structured downsampling in both time and frequency domains. DSST significantly reduces computational and memory demands, while preserving high-resolution representations of fault-related features such as gear meshing frequency sidebands and their harmonics. In contrast to prior SST variants, DSST emphasizes diagnostic interpretability, invertibility, and compatibility with data-driven learning models, making it suitable for deployment in modern condition monitoring frameworks. Experimental results on non-stationary gearbox vibration data demonstrate that DSST achieves comparable diagnostic accuracy to conventional SST methods, with substantial gains in processing efficiency—thereby supporting scalable, real-time industrial health monitoring. Unlike existing downsampling-based SST methods, DSST is designed as a diagnostic component within a scalable, data-driven framework, supporting real-time analysis, signal reconstruction, and downstream machine learning integration. Full article
(This article belongs to the Special Issue Fault Diagnosis Technology in Machinery Manufacturing)
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17 pages, 10288 KiB  
Article
Accelerated Degradation Test and Performance Degradation Characteristics of Intelligent Circuit Breaker Control Circuit
by Zhenhua Xie, Linming Hou, Puquan He, Yizhou Cai and Yao Wang
Processes 2025, 13(5), 1340; https://doi.org/10.3390/pr13051340 - 27 Apr 2025
Viewed by 327
Abstract
With the development of intelligent grid systems, smart circuit breakers are widely used. The control circuit is the core component of the smart circuit breaker, making its performance degradation characteristics highly significant. This paper focuses on the control circuit’s accelerated degradation test and [...] Read more.
With the development of intelligent grid systems, smart circuit breakers are widely used. The control circuit is the core component of the smart circuit breaker, making its performance degradation characteristics highly significant. This paper focuses on the control circuit’s accelerated degradation test and performance degradation characteristics. First, an accelerated degradation test is designed, and a test platform is established. By analyzing the degradation mechanism of the intelligent circuit breaker control loop, the key weak links in the control loop are determined, and then the monitoring quantity is determined. Then, degradation data are preprocessed to extract features from the time, frequency, and wavelet domains. The multidimensional evaluation index model is applied to select the optimal features, fit the degradation trend, and use the fixed segmentation algorithm to divide the degradation stages and analyze the performance degradation characteristics of the control circuit. The experimental results show that the turning points of the two-stage degradation process at 85 °C, 95 °C, and 105 °C are 78.8%, 77.6%, and 77.0%, respectively. The position of the turning point is relatively fixed. The key circuit’s PSpice simulation model is built to verify the two-stage nonlinear characteristics observed in the experimental results. Finally, the results are verified by the Pearson correlation coefficient. The results show that the Pearson correlation coefficient between the simulation and accelerated life test results is above 0.9158, and the consistency between the two is high. Full article
(This article belongs to the Special Issue Fault Diagnosis Technology in Machinery Manufacturing)
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