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.
Highlights
Industries are currently going through “The Fourth Industrial Revolution,” as professionals have called it, a term known as “Industry 4.0.” (I4.0) Integration amongst physical and digital systems of the production contexts is what mainly concerns Industry 4.0 [1]
The literature review is categorized based on maching learning (ML) techniques, ML categories, equipment used, device used in data acquiring, description of the applied data, data size, and data type
Based on performed comprehensive literature review, predictive maintenance continues to be an important method for improving efficiency in all kinds of environments where machines that wear down over time are involved
Summary
Industries are currently going through “The Fourth Industrial Revolution,” as professionals have called it, a term known as “Industry 4.0.” (I4.0) Integration amongst physical and digital systems of the production contexts is what mainly concerns Industry 4.0 [1]. I4.0 and its key technologies play an essential role to make industrial systems autonomous [2,3] and make possible the automatized data collection from industrial machines/components. Based on the collected data type machine learning algorithms can be applied for automated fault detection and diagnosis. It is very cruel to select appropriate maching learning (ML) techniques, type of data, data size, and equipment to apply ML in industrial systems. Selection of inappropriate predictive maintenance (PdM) technique, dataset, and data size may cause time loss and infeasible maintenance scheduling. This study aims to present a comprehensive literature review to discover existing studies and ML applications, and help researchers and practitioners to select appropriate ML techniques, data size, and data type to obtain a feasible ML application
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