1
|
Abd Wahab NH, Hasikin K, Wee Lai K, Xia K, Bei L, Huang K, Wu X. Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices. PeerJ Comput Sci 2024; 10:e1943. [PMID: 38686003 PMCID: PMC11057655 DOI: 10.7717/peerj-cs.1943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/27/2024] [Indexed: 05/02/2024]
Abstract
Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.
Collapse
Affiliation(s)
- Nur Haninie Abd Wahab
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Engineering Services Division, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Center of Intelligent Systems for Emerging Technology, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kaijian Xia
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Affiliated Changshu Hospital, Soochow University Changshu, Jiangsu, China
| | - Lulu Bei
- School of Information Engineering, Xuzhou University of Technology, Xuzhou, China
| | - Kai Huang
- JiangSu XCMG HanYun Technologies Co., LTD., Xuzhou, China
| | - Xiang Wu
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|
2
|
Zhang Q, Hao C, Lv Z, Fan Q. The combination model of CNN and GCN for machine fault diagnosis. PLoS One 2023; 18:e0292381. [PMID: 37796950 PMCID: PMC10553235 DOI: 10.1371/journal.pone.0292381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results. However, they primarily focus on global features derived from sample signals and fail to explicitly mine relationships between signals. In contrast, graph convolutional network (GCN) is able to efficiently mine data relationships by taking graph data with topological structure as input, making them highly effective for feature representation in non-Euclidean space. In this article, to make good use of the advantages of CNN and GCN, we propose a graph attentional convolutional neural network (GACNN) for effective intelligent fault diagnosis, which includes two subnetworks of fully CNN and GCN to extract the multilevel features information, and uses Efficient Channel Attention (ECA) attention mechanism to reduce information loss. Extensive experiments on three datasets show that our framework improves the representation ability of features and fault diagnosis performance, and achieves competitive accuracy against other approaches. And the results show that GACNN can achieve superior performance even under a strong background noise environment.
Collapse
Affiliation(s)
- Qianqian Zhang
- School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China
| | - Caiyun Hao
- School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China
| | - Zhongwei Lv
- School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China
| | - Qiuxia Fan
- School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China
| |
Collapse
|
3
|
Sanakkayala DC, Varadarajan V, Kumar N, Soni G, Kamat P, Kumar S, Patil S, Kotecha K. Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques. Micromachines (Basel) 2022; 13:1471. [PMID: 36144094 PMCID: PMC9503590 DOI: 10.3390/mi13091471] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 06/16/2023]
Abstract
Predicting bearing failures is a vital component of machine health monitoring since bearings are essential parts of rotary machines, particularly large motor machines. In addition, determining the degree of bearing degeneration will aid firms in scheduling maintenance. Maintenance engineers may be gradually supplanted by an automated detection technique in identifying motor issues as improvements in the extraction of useful information from vibration signals are made. State-of-the-art deep learning approaches, in particular, have made a considerable contribution to automatic defect identification. Under variable shaft speed, this research presents a novel approach for identifying bearing defects and their amount of degradation. In the proposed approach, vibration signals are represented by spectrograms, and deep learning methods are applied via pre-processing with the short-time Fourier transform (STFT). A convolutional neural network (CNN), VGG16, is then used to extract features and classify health status. After this, RUL prediction is carried out with the use of regression. Explainable AI using LIME was used to identify the part of the image used by the CNN algorithm to give the output. Our proposed method was able to achieve very high accuracy and robustness for bearing faults, according to numerous experiments.
Collapse
Affiliation(s)
| | - Vijayakumar Varadarajan
- School of NUOVOS, Ajeenkya DY Patil University, Pune 412105, India
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Namya Kumar
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
| | - Girija Soni
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
| | - Pooja Kamat
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
| | - Satish Kumar
- Symbiosis Centre for Applied Artificial Intelligence, Faculty of Engineering, Symbiosis International (Deemed) University, Pune 412115, India
| | - Shruti Patil
- Symbiosis Centre for Applied Artificial Intelligence, Faculty of Engineering, Symbiosis International (Deemed) University, Pune 412115, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Faculty of Engineering, Symbiosis International (Deemed) University, Pune 412115, India
| |
Collapse
|
4
|
Li H, Wang Z, Li Z. An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism. PeerJ Comput Sci 2022; 8:e1084. [PMID: 36091994 PMCID: PMC9455287 DOI: 10.7717/peerj-cs.1084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi-layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.
Collapse
Affiliation(s)
- Hao Li
- Air Force Engineering University, Graduate School, Xi’an, Shaanxi, China
| | - Zhuojian Wang
- Air Force Engineering University, Aeronautics Engineering College, Xi’an, Shaanxi, China
| | - Zhe Li
- Air Force Engineering University, Aeronautics Engineering College, Xi’an, Shaanxi, China
| |
Collapse
|
5
|
Wang L, Tang D, Liu C, Nie Q, Wang Z, Zhang L. An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing. Sensors (Basel) 2022; 22:6472. [PMID: 36080930 PMCID: PMC9460713 DOI: 10.3390/s22176472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/20/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.
Collapse
|
6
|
Cheng X, Chaw JK, Goh KM, Ting TT, Sahrani S, Ahmad MN, Abdul Kadir R, Ang MC. Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry. Sensors (Basel) 2022; 22:s22176321. [PMID: 36080780 PMCID: PMC9460830 DOI: 10.3390/s22176321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/03/2022] [Accepted: 08/07/2022] [Indexed: 05/27/2023]
Abstract
The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.
Collapse
Affiliation(s)
- Xiang Cheng
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Jun Kit Chaw
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Kam Meng Goh
- Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Kampus Utama, Jalan Genting Kelang, Kuala Lumpur 53300, Malaysia
| | - Tin Tin Ting
- Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
| | - Shafrida Sahrani
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Mohammad Nazir Ahmad
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Rabiah Abdul Kadir
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Mei Choo Ang
- Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| |
Collapse
|
7
|
Abstract
To address the problem of the low recognition rate of time-frequency domain methods gearbox fault identification, a method featuring decision-level fusion of DS evidence theory and GA-BP algorithm was proposed in the present study. Firstly, the fault data of each state of the gearbox was classified, based on which the time-frequency domain features were extracted and 19 significant features have been selected. Secondly, the accuracy of the traditional BP algorithm was compared with that of the GA-BP algorithm. On this basis, it has been concluded that the GA-BP algorithm is highly accurate, and the local diagnostic results obtained by the GA-BP algorithm have been used as the basic probability. Finally, the DS evidence theory is currently used to fuses with the GA. In addition, the final fault identification of the gearbox can be achieved by using the DS evidence theory and the multi-sensor local diagnosis results obtained by the GA-BP algorithm for decision fusion. The results of the simulations and experiments showed that the method proposed has improved accuracy over a single algorithm for fault identification of gearboxes, respectively.
Collapse
|
8
|
Abstract
The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.
Collapse
|