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Machines
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Machinery Condition Monitoring and Intelligent Fault Diagnosis
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Machinery Condition Monitoring and Intelligent 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: closed (30 November 2024) | Viewed by 10979

Special Issue Editors

Prof. Dr. Hongli Gao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
Interests: prognostics and health management; mechatronics technology; intelligent robot; high-speed structure design and dynamic analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Zhichao You

E-Mail Website
Guest Editor
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Interests: tool condition monitoring; machine vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machinery condition monitoring and intelligent fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial production processes. Based on machine learning, deep learning, and artificial intelligence, intelligent fault diagnosis has been proposed and achieved remarkable improvements, especially in the face of unknown nonlinear machine behavior and non-stationary data. However, there are still some machinery condition monitoring and intelligent fault diagnosis problems that require further research, such as early fault detection features, a small sample machine learning algorithm, multi-condition transfer learning algorithm, multi-modal data fusion method, and interpretable deep learning algorithm.

To comprehensively report the research progress in this field, disseminate excellent research results, and promote the development and application of machinery condition monitoring and intelligent fault diagnosis, this Special Issue focuses on presenting intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring.

This Special Issue includes, but is not limited to, the following topics:

  • failure mechanisms modeling for mechanical equipment; 
  • monitoring signal processing for mechanical equipment; 
  • intelligent feature extraction for condition monitoring;
  • intelligent early fault detection and diagnosis;
  • few-shot sample learning for fault detection;
  • transfer-learning-based methods for fault diagnosis;
  • interpretable deep learning for fault diagnosis;
  • hybrid models of data-driven and model-based approaches
  • sensor data fusion for fault diagnosis;
  • measurement methods, technologies, and systems for fault diagnosis.

Prof. Dr. Hongli Gao
Dr. Zhichao You
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.

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

Related Special Issue

Published Papers (5 papers)

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Research

22 pages, 8560 KiB  
Article
Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems
by Tingting Wu, Hongliang Song, Hongli Gao, Zongshen Wu and Feifei Han
Machines 2024, 12(12), 895; https://doi.org/10.3390/machines12120895 - 6 Dec 2024
Viewed by 843
Abstract
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning [...] Read more.
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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24 pages, 2249 KiB  
Article
Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework
by Vipul Dave, Himanshu Borade, Hitesh Agrawal, Anshuman Purohit, Nandan Padia and Vinay Vakharia
Machines 2024, 12(6), 373; https://doi.org/10.3390/machines12060373 - 27 May 2024
Cited by 13 | Viewed by 1359
Abstract
Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, [...] Read more.
Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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28 pages, 682 KiB  
Article
Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study
by Nicolò Oreste Pinciroli Vago, Francesca Forbicini and Piero Fraternali
Machines 2024, 12(6), 357; https://doi.org/10.3390/machines12060357 - 22 May 2024
Cited by 5 | Viewed by 3361
Abstract
Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the [...] Read more.
Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three datasets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared on multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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21 pages, 7766 KiB  
Article
Tool Wear Prediction Based on Residual Connection and Temporal Networks
by Ziteng Li, Xinnan Lei, Zhichao You, Tao Huang, Kai Guo, Duo Li and Huan Liu
Machines 2024, 12(5), 306; https://doi.org/10.3390/machines12050306 - 1 May 2024
Cited by 2 | Viewed by 2055
Abstract
Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due [...] Read more.
Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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17 pages, 10635 KiB  
Article
Simulation Modeling and Temperature Over-Advance Perception of Mine Hoist System Based on Digital Twin Technology
by Xuejun Liang, Juan Wu and Kaiyi Ruan
Machines 2023, 11(10), 966; https://doi.org/10.3390/machines11100966 - 17 Oct 2023
Cited by 2 | Viewed by 2117
Abstract
The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. [...] Read more.
The temperature prediction of hoist motor is one of the effective ways to ensure the safe production of mine hoist. Digital twin technology is a technology that combines the physical system of the real world with the digital model of the virtual world. Through digital twin technology, the physical system in the real world can be monitored and simulated in a virtual environment, and the state information of these systems can be monitored in real time. Recurrent neural network is a kind of neural network suitable for processing sequence data, which can automatically extract and learn the feature information in sequential data. To achieve online monitoring and over-advance perception of the temperature of the mine hoist motor, a temperature prediction and advance sensing method based on digital twins and recurrent neural network is proposed. To begin with, a high-fidelity digital twin monitoring system for mine hoists is constructed, enabling the acquisition of real-time temperature data. These temperature data are then fed into a neural network for feature extraction and precise prediction of the motor’s state. Subsequently, based on the temperature prediction module in the digital twin hoist monitoring system, a user interface (UI) is developed, and a fully functional digital twin temperature monitoring system is built and experimentally validated. The experimental results demonstrate that the digital twin system effectively monitors the real-time temperature state of the motor during the operation of the mine hoist. Furthermore, the integration of digital twin and recurrent neural network enables the accurate prediction and proactive detection of temperature variations in the motor of the mine hoist. This innovative approach introduces a novel perspective for implementing predictive maintenance in the mining industry, enhancing the safety and reliability of mine hoists. Additionally, it offers valuable technical support in improving maintenance efficiency and reducing associated costs. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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