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Machines
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Machine Learning for Predictive Maintenance
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Machine Learning for Predictive Maintenance

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 19517

Special Issue Editor

Dr. Tieling Zhang

E-Mail Website
Guest Editor
Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: statistical data modelling; big data analytics; Bayesian inference; Markov Chain Monte Carlo (MCMC) simulation; machine learning; decision support systems; system reliability modelling; degradation modelling and condition prediction; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Almost all infrastructure and systems today are repairable systems. Proper maintenance plays an important role in maintaining the system’s operational efficiency and achieving the required performance. However, maintenance costs usually do not represent a small fraction of the total lifecycle cost (LCC) of the systems. The operation and maintenance cost of wind turbines, for example, is 15~30% of the lifecycle cost. For some other systems, the maintenance costs could be up to 50% of the LCC. Therefore, controlling and reducing maintenance costs has increasingly been of interest to researchers, asset owners, and operators.

Over the last 10 years, with the advancement of new technologies in sensing networks, the cost of the implementation of condition-monitoring systems has been driven continuously down, and at the same time, huge amounts of data have been collected in day-to-day operation. The data bring new value to the asset owners and operators if properly utilized. Therefore, there is a tremendous opportunity for condition-based maintenance (or predictive maintenance) of systems. Various new techniques have been applied to system maintenance for multi-objective optimization. These include techniques using machine learning for fault diagnosis and prediction, health-condition prediction, degradation modelling, maintenance scheduling, optimization of maintenance and operation, etc. This results in a growing trend in using machine learning for system predictive maintenance, for which a number of scholars are making efforts to search for suitable solutions. To cope with the current research trend, this Special Issue is proposed to serve as a forum for researchers to circulate and discuss their research outcomes in system maintenance using advanced technologies including machine learning. This Special Issue proposes to cover, but is not limited to, topics in the following research areas:  

  • Sensing network for data collection
  • Data collection, storage, and management
  • Data processing techniques
  • Data quality assessment
  • Technologies for imputation of missing data
  • Condition monitoring techniques
  • Big data analytics for system operation and maintenance
  • Condition monitoring and condition prediction
  • Degradation modelling
  • Multi-objective optimization
  • Multi-criteria decision-making
  • Fault detection, diagnosis, and prediction
  • Reliability-centered maintenance
  • Determination of remaining useful lifetime
  • System reliability and maintainability modelling
  • Determination of lifecycle cost
  • AI-based models and algorithms for maintenance
  • Application of Internet of Things (IoT) technology for maintenance
  • Decision-making models for maintenance and operation
  • Simulation-based decision-making
  • New models applied to failure and maintenance data
  • Development of time series models
  • Maintenance strategy development
  • Digital twin development for condition monitoring and predictive maintenance
  • Determination of system performance and reliability indices

We warmly welcome your contribution to this Special Issue. If you have any inquiries, please contact Dr Tieling Zhang by email at [email protected].

Dr. Tieling Zhang
Guest Editor

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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Published Papers (5 papers)

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Research

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19 pages, 2923 KiB  
Article
Reinforcement Learning-Based Auto-Optimized Parallel Prediction for Air Conditioning Energy Consumption
by Chao Gu, Shentao Yao, Yifan Miao, Ye Tian, Yuru Liu, Zhicheng Bao, Tao Wang, Baoyu Zhang, Tao Chen and Weishan Zhang
Machines 2024, 12(7), 471; https://doi.org/10.3390/machines12070471 - 12 Jul 2024
Viewed by 840
Abstract
Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient [...] Read more.
Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient feature extraction. Furthermore, due to reasons such as implicit correlations between hyperparameters, automatic hyperparameter optimization (HPO) approaches can not be easily achieved. In this paper, we propose an auto-optimization parallel energy consumption prediction approach based on reinforcement learning. It can parallel process multidimensional time series data and achieve the automatic optimization of model hyperparameters, thus yielding an accurate prediction of air conditioning energy consumption. Extensive experiments on real air conditioning datasets from five factories have demonstrated that the proposed approach outperforms existing prediction solutions, with an increase in average accuracy by 11.48% and an average performance improvement of 32.48%. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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26 pages, 6414 KiB  
Article
A New Methodological Framework for Optimizing Predictive Maintenance Using Machine Learning Combined with Product Quality Parameters
by Carlo Riccio, Marialuisa Menanno, Ilenia Zennaro and Matteo Mario Savino
Machines 2024, 12(7), 443; https://doi.org/10.3390/machines12070443 - 27 Jun 2024
Cited by 1 | Viewed by 5359
Abstract
Predictive maintenance (PdM) is the most suitable for production efficiency and cost reduction, aiming to perform maintenance actions when needed, avoiding unwanted failures and unnecessary preventive actions. The increasing use of 4.0 technologies in industries has allowed the adoption of recent advances in [...] Read more.
Predictive maintenance (PdM) is the most suitable for production efficiency and cost reduction, aiming to perform maintenance actions when needed, avoiding unwanted failures and unnecessary preventive actions. The increasing use of 4.0 technologies in industries has allowed the adoption of recent advances in machine learning (ML) to develop an effective PdM strategy. Then again, production efficiency not only considers production volumes in terms of pieces or working hours, but also product quality (PQ), which is an important parameter to also detect possible defects in machines. In fact, PQ can be used as a parameter to predict possible failures and deeply affects manufacturing costs and reliability. In this context, this study aims to create a product performance-based maintenance framework through ML to determine the optimal PdM strategy based on the desired level of product quality and production performance. The framework is divided into three parts, starting from data collection, through the choice of the ML algorithm and model construction, and finally, the results analysis of the application to a real manufacturing process. The model has been tested within the production line of electromechanical components. The results show that the link between the variables representing the state of the machine and the qualitative parameters of the production process allows us to control maintenance actions based on scraps optimization, achieving an improvement in the reliability of the machine. Moreover, the application in the manufacturing process allows us to save about 50% of the costs for machine downtime and 64% of the costs for scraps. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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19 pages, 2542 KiB  
Article
Integrated Optimization Model for Maintenance Policies and Quality Control Parameters for Multi-Component System
by Mustafa M. Nasr, Fadia Naji, Mokhtar Amrani, Mageed Ghaleb, Khaled N. Alqahtani, Asem Majed Othman and Emad Hashiem Abualsauod
Machines 2023, 11(4), 435; https://doi.org/10.3390/machines11040435 - 29 Mar 2023
Cited by 1 | Viewed by 1880
Abstract
The practical applications of integrated maintenance policies and quality for a multi-component system are more complicated, still rare, and incomplete to meet the requirements of Industry 4.0. Therefore, this work aims to extend the integration economic model for optimizing maintenance policies and quality [...] Read more.
The practical applications of integrated maintenance policies and quality for a multi-component system are more complicated, still rare, and incomplete to meet the requirements of Industry 4.0. Therefore, this work aims to extend the integration economic model for optimizing maintenance policies and quality control parameters by incorporating the Taguchi loss function for a multi-component system. An optimization model is developed based on preventive maintenance, corrective maintenance policies, and quality control parameters with the CUSUM (Cumulative Sum) chart, which is widely used for detecting small shifts in the process mean. The model was developed to minimize the expected total cost per unit of time and to obtain the optimal values of decision variables: the size of samples, sample frequency, decision interval, coefficient of the CUSUM chart, and preventive and corrective maintenance intervals. The solution steps were employed by selecting a case study in the Alahlia Mineral Water Company (AMWC). Then, the design of experiments based on one-factor-at-a-time was used to evaluate the effect of selected decision variables on the expected total cost. Finally, sensitivity analysis was performed on the selected decision variables to demonstrate the robustness of the developed model. A predictive maintenance plan was developed based on the optimal value of preventive maintenance interval, and the results showed that the performance of the maintenance plan realizes the full potential of the integrated model. In addition, the case study results indicate that the extended integrated model for multicomponent is the new standard for the quality production of multi-component systems in future works. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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20 pages, 2595 KiB  
Article
Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing
by Bita Ghasemkhani, Ozlem Aktas and Derya Birant
Machines 2023, 11(3), 322; https://doi.org/10.3390/machines11030322 - 23 Feb 2023
Cited by 19 | Viewed by 4390
Abstract
Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a [...] Read more.
Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a timely manner. However, a standard ML algorithm cannot be directly applied to a PdM dataset, which is highly imbalanced since, in most cases, signals correspond to normal rather than critical conditions. To deal with data imbalance, in this paper, a novel explainable ML method entitled “Balanced K-Star” based on the K-Star classification algorithm is proposed for PdM in an IoT-based manufacturing environment. Experiments conducted on a PdM dataset showed that the proposed Balanced K-Star method outperformed the standard K-Star method in terms of classification accuracy. The results also showed that the proposed method (98.75%) achieved higher accuracy than the state-of-the-art methods (91.74%) on the same data. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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Review

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33 pages, 2357 KiB  
Review
Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning
by Muhammad Hussain, Tieling Zhang, Muzaffar Chaudhry, Ishrat Jamil, Shazia Kausar and Intizar Hussain
Machines 2024, 12(1), 42; https://doi.org/10.3390/machines12010042 - 8 Jan 2024
Cited by 12 | Viewed by 4768
Abstract
Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven [...] Read more.
Pipeline integrity and safety depend on the detection and prediction of stress corrosion cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion-monitoring techniques are used. The observed data exhibit characteristics of nonlinearity, multidimensionality, and noise. Hence, data-driven modeling techniques have been widely utilized. To accomplish intelligent corrosion prediction and enhance corrosion control, machine learning (ML)-based approaches have been developed. Some published papers related to SCC have discussed ML techniques and their applications, but none of the works has shown the real ability of ML to detect or predict SCC in energy pipelines, though fewer researchers have tested their models to prove them under controlled environments in laboratories, which is completely different from real work environments in the field. Looking at the current research status, the authors believe that there is a need to explore the best technologies and modeling approaches and to identify clear gaps; a critical review is, therefore, required. The objective of this study is to assess the current status of machine learning’s applications in SCC detection, identify current research gaps, and indicate future directions from a scientific research and application point of view. This review will highlight the limitations and challenges of employing machine learning for SCC prediction and also discuss the importance of incorporating domain knowledge and expert inputs to enhance the accuracy and reliability of predictions. Finally, a framework is proposed to demonstrate the process of the application of ML to condition assessments of energy pipelines. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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