Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Abstract

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

“…While the concept of condition monitoring has been around for some time, the market for more sophisticated predictive maintenance products is still very young. There are four types of maintenance classified in the literature: corrective, scheduled, condition-based, and statistical-based maintenance [ 100 , 101 ]. Predictive maintenance has evolved from corrective maintenance using new technologies and procedures for predicting and preventing failure.…”

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

“…Studies on machine learning and its applications are proliferating. Focusing on its implications for solving issues in manufacturing businesses, research has focused on predicting failures [6]. Cinar et al (2020) [14] and Binding, Dykeman, and Pang (2019) [19] forecasted the downtime of manufacturing machines using real-time prediction models.…”

“…Cinar et al (2020) [14] and Binding, Dykeman, and Pang (2019) [19] forecasted the downtime of manufacturing machines using real-time prediction models. They utilized unstructured historical machine data to train the machine learning classification algorithms, including random forest, XGBoost, and logistic regression, to predict machine failures [6]. Qi, X et al (2019) [20] conducted a study to apply neural network algorithms to complete additive manufacturing process chains from design to post-treatment.…”

“…One concern of deploying machines or deep learning techniques in manufacturing fields is the selection of appropriate algorithms. That is, as techniques are very sensitive to the types of input data and size of the data, appropriate techniques should be selected for a particular manufacturing type [6]. In other words, it is crucial to choose a manufacturing business for specific types of algorithms to fully enhance the manufacturing process.…”

Mathematical Analysis of Criteria for Maintenance of Technical Systems in the Function of Achieving Sustainability

“…The main goal of research [14] is to define a sustainable approach for the maintenance of asphalt pavement construction. Predictive maintenance is inevitable for sustainable smart manufacturing in I4.0 in research [15].…”