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Machines
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Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault...
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Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5767

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
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 Issue Information

Dear Colleagues,

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, and 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 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 diagnosis;
  • 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
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. 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

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Published Papers (7 papers)

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Research

34 pages, 16514 KiB  
Article
Enhanced Non-Destructive Testing of Small Wind Turbine Blades Using Infrared Thermography
by Majid Memari, Mohammad Shekaramiz, Mohammad A. S. Masoum and Abdennour C. Seibi
Machines 2025, 13(2), 108; https://doi.org/10.3390/machines13020108 - 29 Jan 2025
Viewed by 297
Abstract
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this [...] Read more.
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs). Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
26 pages, 10741 KiB  
Article
Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill
by Rongrong Peng, Changfen Gong and Shuai Zhao
Machines 2025, 13(1), 30; https://doi.org/10.3390/machines13010030 - 6 Jan 2025
Viewed by 388
Abstract
Graph data and multi-sensor information fusion have been integrated into the abnormal vibration type classification and the identification of the rolling mill for extracting spatial–temporal and robust features. However, most of the existing deep learning (DL) based methods exploit only single sensor information [...] Read more.
Graph data and multi-sensor information fusion have been integrated into the abnormal vibration type classification and the identification of the rolling mill for extracting spatial–temporal and robust features. However, most of the existing deep learning (DL) based methods exploit only single sensor information and Euclidean space data, which results in incomplete information contained in the features extracted by in-depth networks. To solve this issue, a multi-sensor information fusion with multi-scale adaptive graph convolutional networks (M2AGCNs) framework is proposed to model graph data and multi-sensor information fusion in a unified in-depth network and then to achieve abnormal vibration diagnosis. First, convolutional neural networks (CNNs) were adopted for the deeper features of multi-sensor signals. And then, the extracted features were fed into the proposed feature-driven adaptive graph generation network to build graphs to extract spatial–temporal correlation between multi-sensor data. After that, the multi-scale graph convolutional networks (MSGCNs) were employed to aggregate and enrich several different receptive information to further improve valuable features. Finally, the extracted multi-sensor features were integrated into a unified network to achieve the abnormal vibration type classification and identification of the rolling mill. Meanwhile, we performed horizontal, vertical, and coupled abnormal vibration experiments, and then three different types of studies were conducted to illustrate the superiority and usefulness of this method in the paper and the feasibility of rolling mill abnormal vibration diagnosis. It can be seen from the results that the proposed M2AGCNs can be able to achieve valuable feature extraction effectively from multi-sensor information and to obtain more excellent behavior of the abnormal vibration diagnosis of the rolling mill in comparison with the mainstream methods. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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14 pages, 2267 KiB  
Article
Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism
by Wenyou Du, Jingyi Zhang, Guanglei Meng and Haoran Zhang
Machines 2024, 12(12), 879; https://doi.org/10.3390/machines12120879 - 4 Dec 2024
Viewed by 629
Abstract
The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention [...] Read more.
The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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21 pages, 6144 KiB  
Article
Unbalanced Position Recognition of Rotor Systems Based on Long and Short-Term Memory Neural Networks
by Yiming Cao, Changzhi Shi, Xuejun Li, Mingfeng Li and Jie Bian
Machines 2024, 12(12), 865; https://doi.org/10.3390/machines12120865 - 28 Nov 2024
Viewed by 562
Abstract
Rotor unbalance stands as one of the primary causes of vibration and noise in rotating equipment. Accurate identification of unbalanced positions enables targeted measures for balance correction, thereby reducing vibration and noise levels and enhancing the operational efficiency and stability of the equipment. [...] Read more.
Rotor unbalance stands as one of the primary causes of vibration and noise in rotating equipment. Accurate identification of unbalanced positions enables targeted measures for balance correction, thereby reducing vibration and noise levels and enhancing the operational efficiency and stability of the equipment. However, the complexity of rotor structures may lead to a diversity of vibration transmission paths, which complicates the identification of unbalanced positions. In this paper, an experimental platform for rotor systems is established to analyze the change patterns of vibration displacement in rotor systems at four unbalanced positions. Additionally, a rotor dynamics model is developed based on the finite element method and verified through experiments. Furthermore, an unbalanced rotor position identification method based on Long Short-Term Memory (LSTM) neural networks is proposed. This method utilizes multiple sets of measured response data and simulated data from unbalanced rotor positions to train the LSTM network, achieving precise identification of unbalanced positions at various rotational speeds. The research results indicate that under subcritical, critical, and supercritical speeds, the identification accuracy based on measured data reaches 95.5%, while the accuracy based on simulated data remains at a high level of 90.5%. These results fully validate the effectiveness and accuracy of the proposed model and identification method, providing new insights and technical means for identifying unbalanced rotor positions. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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16 pages, 8804 KiB  
Article
Research on Unbalanced Vibration Characteristics and Assembly Phase Angle Probability Distribution of Dual-Rotor System
by Hui Li, Changzhi Shi, Xuejun Li, Mingfeng Li and Jie Bian
Machines 2024, 12(12), 842; https://doi.org/10.3390/machines12120842 - 24 Nov 2024
Viewed by 465
Abstract
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, [...] Read more.
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, a dynamic model of the four-disk, five-pivot dual-rotor system is established, with its natural frequencies and vibration modes verified. The influence of size and the position of the unbalance on the vibration amplitude in the dual-rotor system is analyzed. Additionally, the probability distribution of the assembly phase angles for both the compressor and turbine sections of the low-pressure rotor is examined. The results indicate that for the low-pressure rotor exhibiting excessive vibration, adjusting the assembly phase angle of the rotors’ system’s compressor or the turbine section by 180 degrees leads to a vibration qualification rate of 70.1435%. This finding is consistent with the observations from the field experience method used in the former Soviet Union. Finally, corresponding experimental verification is conducted. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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23 pages, 8375 KiB  
Article
Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes
by Samuel Ayankoso, Fengshou Gu, Hassna Louadah, Hamidreza Fahham and Andrew Ball
Machines 2024, 12(9), 630; https://doi.org/10.3390/machines12090630 - 7 Sep 2024
Viewed by 1759
Abstract
The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data [...] Read more.
The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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25 pages, 19178 KiB  
Article
A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals
by Zhongyao Wang, Zejun Zheng, Dongli Song and Xiao Xu
Machines 2024, 12(8), 571; https://doi.org/10.3390/machines12080571 - 19 Aug 2024
Viewed by 631
Abstract
The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box [...] Read more.
The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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