Predictive Maintenance in a Digital Factory Shop-Floor: Data Mining on Historical and Operational Data Coming from Manufacturers’ Information Systems

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

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“…Research work [14] studies a predictive maintenance application in the white goods/home appliances sector.…”

Section: Related Workmentioning

confidence: 99%

Agnostic learning for packing machine stoppage prediction in smart factories

Gabriel¹,

Katsidimas²,

Nikoletseas³

et al. 2022

ITU J-FET

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“…Most of the research work performed on PdM is mainly based on sensor data of machinery conditions. But Pertselakis et al (2019) used both historical logs and sensor data for forecasting failures on a machine shop floor. Generally, PdM is defined as strategies to prevent machine breakdown by predicting real-time failure situations and minimizing corrective maintenance costs.…”

Section: Literature Review 21 Maintenancementioning

confidence: 99%

“…Generally, PdM is defined as strategies to prevent machine breakdown by predicting real-time failure situations and minimizing corrective maintenance costs. The main objective of PdM is to detect abnormal behavior of equipment by continuously analyzing sensor data Pertselakis et al (2019). In this study, a methodology has been proposed in place of PdM as it requires a high installation cost, for detecting potential failures from regular maintenance data using a text mining approach.…”

Section: Literature Review 21 Maintenancementioning

confidence: 99%

Application of classification models on maintenance records through text mining approach in industrial environment

Rahman

Mahbub

2022

JQME

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PurposeThe data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.Design/methodology/approachThis paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.FindingsThe accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.Practical implicationsThe proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.Originality/valueNowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

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“…Predictive Maintenance (PdM) anticipates possible flaws based on degradation development trends and indicates the best moment for an ideal intervention [1], [2]. For this reason, the prognosis of rotating machines' health state has been the focal point of research in recent years.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher

Guerroum¹,

Zegrari²,

Masmoudi³

et al. 2022

IJETAE

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Predictive maintenance currently involves digital transformation with all the technologies developed to serve the latter. This maintenance strategy is believed to be an efficient solution to end late/early intervention issues. It is for this reason that machine health state monitoring by Remaining Useful Life prognosis is very crucial. However, in the literature, most studies focus on failure diagnosis more than the system's Remaining Useful Life. In addition, to prepare models to serve the prognosis, the use of actual machinery data is critical to assure the later scalability of the application. The literature about predictive maintenance has often evaluated data-driven approaches with machine learning techniques processing simulated Data rather than real ones. To tackle this problem, the authors propose a continuity of previous work treating a jaw crusher default diagnosis in the context of the ore mining industry. The RUL of the crusher components is estimated upon completion of the fault diagnosis data. Smart sensors Data have been preprocessed to serve the evaluation of four regression machine learning models: Bayesian Linear Regression, Poisson Regression, Neural Network Regression, and Random Forest. Poisson regression and Neural Network required data normalization in this case study to improve their performance. Linear regression methods proved their inability to forecast the machine degradation state, while the bagging ensemble method, Random Forest, was able to track the actual values. This paper aims to enhance the Prognosis and Health Management of the machine while contributing to the literature enhancement on failure prognosis using real industrial data. Keywords— Data-driven approach, Industry 4.0, Machine learning, Predictive Maintenance, RUL prognosis, Smart sensors.

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Predictive Maintenance in a Digital Factory Shop-Floor: Data Mining on Historical and Operational Data Coming from Manufacturers’ Information Systems

Cited by 9 publications

References 10 publications

Agnostic learning for packing machine stoppage prediction in smart factories

Agnostic learning for packing machine stoppage prediction in smart factories

Application of classification models on maintenance records through text mining approach in industrial environment

Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher

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