<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si12.svg" display="inline" id="d1e1435"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>-LSTM for predictive maintenance of industrial equipment based on machine learning

Jiang, Yuchen; Dai, Pengwen; Fang, Pengcheng; Zhong, Ray Y.; Zhao, Xiaoli; Cao, Xiaochun

doi:10.1016/j.cie.2022.108560

Cited by 27 publications

(10 citation statements)

References 30 publications

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“…Unlike traditional methods, ML algorithms, especially the supervised learning approach, can learn from historical data to identify intricate patterns and relationships that are indicative of impending failure, which are not discernible through simple threshold-based monitoring. The supervised PdM tasks can be further specified into two types, which are prognosis (i.e., classification task conducting discrimination between healthy and unhealthy conditions of the system being monitored) ,,− and estimation of the exact remaining useful life (i.e., regression task). − The performance of the supervised PdM is usually evaluated based on the trade-off between unexpected breaks (percentage of not prevented failures) and unexploited lifetime (average time that may have been run if the maintenance action suggested by the PdM had not been performed).…”

Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

“…Typically, data-driven PdM relies extensively on historical monitoring data to observe the evolution of the equipment from a safe initial condition to the break of the equipment part/machine failure. Commonly used features for constructing the prediction model include property features (e.g., vibration signal data, temperature, current, and voltage) ,,,,,,− ,− and historic process features (e.g., recipe and log data). ,, Currently, data-driven PdM has been widely applied to a variety of equipment from various industries, such as bearings, filaments, turbines, motors, gearboxes, and compressors. ,,,,,, The most employed algorithms (one study can apply more than one ML algorithm) are RF (35.7%) − ,,,, and SVM (32.1%), ,,,,,,, while DL, such as long short-term memory (LSTM), has become increasingly popular in recent years (32.1%). ,− ,,, Such proposed models can provide a prediction accuracy range of 77.2% to 99.84% for identifying malfunctions which significantly outperforms traditional models. ,,, …”

Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

See 1 more Smart Citation

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

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Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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“…Classical TPM [91] TPM is a dynamic capability that forges a new bundle with Industry 4.0 and the circular economy to ensure sustainable performance for manufacturing businesses.…”

Section: Lack Of Sustainability In the Concept Of Tpmmentioning

confidence: 99%

A Review of Sustainable Total Productive Maintenance (STPM)

et al. 2023

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Sustainable Total Productive Maintenance (STPM) arose in 2021 as a promising, new concept to fill the lack of sustainability in Total Productive Maintenance (TPM) and allow companies to overcome its implementation barriers. It revolves around increasing the understanding of the systems approach and contributing to setting contemporary companies’ sustainable ideology by supporting orientation toward sustainability from a sustainable maintenance perspective. However, STPM is still in its infancy and is viewed as a complement to the traditional TPM approach and is based on its pillars. Moreover, there is still a dearth of literature discussing STPM. This study aims to present STPM as a novel substitute for TPM while building its unique mechanism based on re-engineered fourth generation management (R4thGM). To pursue such a goal, 94 papers from Scopus, Web of Science, and Science Direct databases published in 2008–2023 were reviewed. This study’s novelty comes from presenting STPM as the best-suited lean manufacturing and sustainability strategy for enhancing sustainable maintenance, encouraging contemporary maintenance (i.e., Industry 4.0 technology-based sustainable maintenance), and supporting second-era contemporary companies’ orientation toward sustainability. Furthermore, based on recent studies, propositions are formulated to achieve STPM. Finally, research implications and future directions are presented.

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“…Many supervised ML algorithms have been applied to PdM, including Support Vector Machines (SVM) [ 25 , 26 ], Ensemble Learning (EL) [ 27 ], and Deep Learning (DL) [ 28 , 29 ] etc. However, these models are limited as follows.…”

Section: Introductionmentioning

confidence: 99%

Anomaly prediction of CT equipment based on IoMT data

Wang,

Liu,

Zhou

et al. 2023

BMC Med Inform Decis Mak

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Background Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model. Methods We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models. Results The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset. Conclusions The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

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A2-LSTM for predictive maintenance of industrial equipment based on machine learning

Cited by 27 publications

References 30 publications

Data Science Applications in Circular Economy: Trends, Status, and Future

Data Science Applications in Circular Economy: Trends, Status, and Future

A Review of Sustainable Total Productive Maintenance (STPM)

Anomaly prediction of CT equipment based on IoMT data

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