On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: An Analytical Literature Review of Maintenance Strategies

Abstract

Due to advances in machine learning techniques and sensor technology, the data driven perspective is nowadays the preferred approach for improving the quality of maintenance for machines and processes in industrial environments. Our study reviews existing maintenance works by highlighting the main challenges and benefits and consequently, it shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. Moreover, we argue that in any industrial setup, the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. In order to classify the existing maintenance strategies, we explore the entire data driven model development life cycle: data acquisition and analysis, model development and model evaluation. Based on the surveyed literature we introduce taxonomies that cover relevant predictive models and their corresponding data driven maintenance techniques.

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

“…Carvalho et al [13] presents a structured review of new literature presented in the years after 2014 and does so in a general manner, so the reader can get a general understanding of what moves within the field. Merkt et al [4] studies various maintenance approaches and the different applications that PdM might have. This is done by studying the full cycle of developing a successful model.…”

“…[15] 2019 Review and proposition of efficient step-wise method for companies to deploy models on historical data and digital twins. [4] 2019 Review of various maintenance strategies, their application and use-cases with focus on the full cycle of creating PdM models. [16] 2018 An introduction to CDM with a strong overview over what possibilities it provides.…”

“…Hence, the question becomes when maintenance should be deployed. Traditionally maintenance had been done either reactive or preventive [4]. The reactive maintenance approach allows components and machinery to run till failure.…”

“…This significantly reduces downtime, as the metric is decided based on expert knowledge. The downside is that asset operators do not know whether the maintenance is deployed efficiently [2,4]. If the engine could go longer without maintenance a result would be poor resource management.…”

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

“…Merkt reviewed maintenance approaches with applicability in the industrial environment. They found that literature did not inform as to which new AI technology based on ML methods and techniques is influencing and changing the maintenance approaches in the industrial setup [19]. Carvalho et al reviewed the use of ML techniques in PdM applications [20].…”