Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault...
share announcement

Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis (2nd Edition)

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2859

Special Issue Editors

Dr. Bingyan Chen

E-Mail Website
Guest Editor
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: non-stationary signal processing; machine condition monitoring; rotating machinery fault diagnosis; acoustic-vibration sensing technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yao Cheng

E-Mail Website
Guest Editor
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China
Interests: fault diagnosis and health monitoring of rotating machinery; fault diagnosis and performance evaluation of rail vehicle transmission system; big data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Fan Zhang

E-Mail Website
Guest Editor
School of Design, Southwest Jiaotong University, Chengdu 610031, China
Interests: intelligent fault diagnosis; pattern recognition; multimodal perception

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis” (https://www.mdpi.com/journal/machines/special_issues/65LUC20871), we are pleased to announce the next in the series, entitled “Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis (2nd Edition)”.

With the enrichment of functions and the integration of intelligence, the safety of high-end equipment in various industrial fields, such as high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools, is receiving unprecedented attention from academia and industry. Fault diagnosis is an effective means to ensure the safe operation of machines as it can significantly minimize operation and maintenance costs and enhance the economic benefits. Scholars, researchers, and engineers are seeking advanced and efficient fault diagnosis technologies to ensure the performance and efficiency of machines, especially high-end equipment. With the advancement of monitoring and sensing technology, machine status data are continuously accumulated, providing effective support for the development of fault diagnosis technology based on signal processing and artificial intelligence. Therefore, this Special Issue aims to publish research work on condition monitoring and fault diagnosis of high-end equipment through advanced signal processing and artificial intelligence technologies.

This Special Issue welcomes original and high-quality research articles and review articles that address a wide range of topics related to the fault diagnosis of high-end equipment. The submitted articles are expected to provide novel and newly developed ideas, algorithms, methods, and technologies that contribute to a better understanding of condition monitoring and fault diagnosis in high-end equipment. The scope of this Special Issue includes, but is not limited to, the following:

  • Vibration, acoustic, and current-based machine fault diagnoses;
  • Novel sensing technology for machine fault diagnosis;
  • Novel signal processing and artificial intelligence algorithms for machine fault diagnosis or condition monitoring;
  • Fault diagnosis or condition monitoring of high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools;
  • Fault diagnosis or condition monitoring of bearings, gears, and rotors.

We look forward to receiving your contributions.

Dr. Bingyan Chen
Dr. Yao Cheng
Dr. Fan Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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. Machines 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

  • signal processing
  • artificial intelligence
  • machine learning
  • machine condition monitoring
  • machine fault diagnosis
  • high-end equipment

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 6823 KB  
Article
A Verifiable Steady-State Frequency–Velocity Mapping for Desktop FDM Printers Based on an Electromechanical Coupling Framework
by Xinfeng Zou, Haiyan Miao, Baoshan Huang, Zhen Li and Fengshou Gu
Machines 2026, 14(5), 508; https://doi.org/10.3390/machines14050508 - 2 May 2026
Viewed by 319
Abstract
To monitor online the operational condition and quality of a desktop fused deposition modeling (FDM) printer, the dynamics of vibro-acoustics must be accurately understood. In this paper, an electromechanical coupling (EMT) framework is established to relate the dynamics of stepper actuation, the transmission [...] Read more.
To monitor online the operational condition and quality of a desktop fused deposition modeling (FDM) printer, the dynamics of vibro-acoustics must be accurately understood. In this paper, an electromechanical coupling (EMT) framework is established to relate the dynamics of stepper actuation, the transmission chain, and machine motion, deriving a steady-state frequency–velocity mapping for steady or near steady printing segments. The mapping is evaluated by numerical calculation to obtain a theoretical drive frequency for different toolpath directions and commanded printing velocities. Validation is performed on the experiment platform I. Drive-side vibration is measured by an accelerometer mounted on the x-axis beam near the motor end. An acoustic channel is recorded as an auxiliary qualitative cross-check rather than for quantitative error evaluation. For steady printing segments, the dominant frequency in drive-side vibration is compared with the theoretical drive frequency. In the tested steady segments and toolpath directions, the relative error remained below 3%. In a further case study, the G-code is modified to introduce two constant printing velocity segments (40 mm/s and 80 mm/s) within the same continuous record, enabling a direct comparison of dominant frequencies between two steady segments. The results show that, under open-loop stepper drive and within the steady/near steady scope adopted here, a drive-related dominant frequency can be observed stably in the x-axis beam vibration response and matches the theoretical drive frequency. When the commanded constant printing velocity is doubled, the dominant frequency in drive-side vibration in the corresponding steady segment changes by approximately a proportional factor. This study provides a verifiable drive referenced frequency–velocity mapping for steady segments under the tested configuration and a traceable frequency reference for steady segment comparisons within the same print record in subsequent case studies. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis (2nd Edition))
►▼ Show Figures

Figure 1

26 pages, 9103 KB  
Article
A Fault Diagnosis Method for Rolling Bearings Based on Improved Speed Time-Varying Filtering Empirical Mode Decomposition and Adaptive Sine–Cosine Optimization Algorithm
by Lifeng Wang, Mingchen Lv, Wenming Cheng, Xiao Xu, Zejun Zheng and Dongli Song
Machines 2026, 14(3), 283; https://doi.org/10.3390/machines14030283 - 3 Mar 2026
Viewed by 507
Abstract
As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issue, a novel fault diagnosis method is proposed [...] Read more.
As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issue, a novel fault diagnosis method is proposed in this paper, which integrates an improved speed time-varying filtering empirical mode decomposition (ISTVF-EMD) with an adaptive sine–cosine optimization algorithm (A-SCA), enabling precise and efficient extraction of fault features. The core of the proposed method lies in improving the conventional time-varying filtering empirical mode decomposition (TVF-EMD) by setting a maximum decomposition layer limit, effectively addressing issues of excessive components and low computational efficiency during the decomposition of low signal-to-noise ratio (SNR) signals. Furthermore, a multi-characteristic frequency energy concentration centrality (MCFECC) index is employed as a fitness function to guide A-SCA in adaptively searching for the optimal bandwidth threshold and fitting order parameters of ISTVF-EMD, thereby extracting components with the most enriched fault information. Validated through simulation and multiple test bench cases, the results indicate that the proposed method can not only significantly enhance the fault characteristic frequencies and their harmonics in the envelope spectrum, successfully diagnosing outer race, inner race, and rolling element faults, but also, compared with the original method, ISTVF-EMD substantially reduces the computational time while ensuring or even improving the decomposition quality. The method presented in this paper provides an effective solution for achieving precise and adaptive fault diagnosis of rolling bearings under strong noise interference. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis (2nd Edition))
►▼ Show Figures

Figure 1

16 pages, 4737 KB  
Article
An Influence Analysis of the Bearing Waviness on the Vibrations of a Flexible Gear
by Shenlong Li, Yajun Xu, Ruikun Pang and Jing Liu
Machines 2025, 13(8), 661; https://doi.org/10.3390/machines13080661 - 28 Jul 2025
Cited by 1 | Viewed by 1360
Abstract
Roller bearing manufacturing errors have been proven to be critical factors affecting the vibrations of gear systems. Waviness is one main form of manufacturing error affecting the operational performance and life of bearings. However, most previous studies did not completely incorporate the effects [...] Read more.
Roller bearing manufacturing errors have been proven to be critical factors affecting the vibrations of gear systems. Waviness is one main form of manufacturing error affecting the operational performance and life of bearings. However, most previous studies did not completely incorporate the effects of the uneven bearing waviness on the flexible gear system vibrations. To characterize the contribution of the uneven bearing waviness on the vibrations of the gear system, a gear transmission system dynamics model considering shaft flexibility was established. The evenness sinusoidal waviness model (SWM) and uneven sinusoidal waviness model considering the time-varying contact (SWMS) were compared. The influences of the time-varying gear meshing stiffness excitations and flexibilities of shafts on the vibrations of the gear system were considered. A dynamic model was established, and the vibrations of the flexible gear system with the SWM and SWMS were compared. The vibrations induced by different amplitudes and orders of bearing waviness were analyzed. Note that the waviness of the bearing has a great influence on the system vibrations. The vibrations of the flexible gear system intensified with the increase in the bearing waviness order and amplitude. The vibrations from the gear system with the SWMS were bigger than those of the SWM. This paper introduces an alternative dynamic modeling model enabling the vibration analysis of the flexible gear system with evenness and uneven bearing waviness. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis (2nd Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop