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Hamasha MM, Bani-Irshid AH, Al Mashaqbeh S, Shwaheen G, Al Qadri L, Shbool M, Muathen D, Ababneh M, Harfoush S, Albedoor Q, Al-Bashir A. Strategical selection of maintenance type under different conditions. Sci Rep 2023; 13:15560. [PMID: 37731044 PMCID: PMC10511507 DOI: 10.1038/s41598-023-42751-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/14/2023] [Indexed: 09/22/2023] Open
Abstract
Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration.
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Affiliation(s)
- Mohammad M Hamasha
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan.
| | - Ala H Bani-Irshid
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Sahar Al Mashaqbeh
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Ghada Shwaheen
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Laith Al Qadri
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Mohammad Shbool
- Department of Industrial Engineering, Faculty of Engineering, The University of Jordan, Amman, 11942, Jordan
| | - Dania Muathen
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Mussab Ababneh
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Shahed Harfoush
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Qais Albedoor
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
| | - Adnan Al-Bashir
- Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
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Huang L, Pan X, Liu Y, Gong L. An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction. SENSORS (BASEL, SWITZERLAND) 2023; 23:7239. [PMID: 37631775 PMCID: PMC10459474 DOI: 10.3390/s23167239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/03/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023]
Abstract
The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance.
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Affiliation(s)
- Lin Huang
- Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China; (L.H.)
| | - Xin Pan
- School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
| | - Yajie Liu
- Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China; (L.H.)
| | - Li Gong
- Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China; (L.H.)
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Werbińska-Wojciechowska S, Winiarska K. Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031409. [PMID: 36772449 PMCID: PMC9919563 DOI: 10.3390/s23031409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 05/14/2023]
Abstract
Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012-2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.
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Liu W, Xu L, Zhang B. Research on Health State Classification and Maintenance Strategy Optimisation of Manufacturing Equipment Based on Brittleness. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06946-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.
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Sarita K, Devarapalli R, Kumar S, Malik H, García Márquez FP, Rai P. Principal component analysis technique for early fault detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-189755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis.
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Affiliation(s)
- Kumari Sarita
- Electrical Engineering Department, BIT Sindri, Dhanbad, Jharkhand, India
| | - Ramesh Devarapalli
- Electrical Engineering Department, BIT Sindri, Dhanbad, Jharkhand, India
- Electrical Engineering Department, IIT (ISM), Dhanbad, Jharkhand, India
| | | | | | | | - Pankaj Rai
- Electrical Engineering Department, BIT Sindri, Dhanbad, Jharkhand, India
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Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020814] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.
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