Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

Abstract

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

Intelligent and Automated Product Quality Assurance System using Machine Learning

“…Numerous studies have recently looked into the potential benefits of these innovations to enhance maintenance processes, decrease downtime, and boost efficiency. Two examples of this type of research are Cheng et al's (2022) [5] comprehensive review of the literature on visual analytics as a form of predictive maintenance in the identical industry as well as Nacchia et al's (2021) [4] systematic representation of the developing deployment of ML methods to conduct predictive maintenance in the industry of manufacturing. various studies seek to provide a thorough overview of the state-of-the-art in various subjects today, to spot trends and problems, and to point up promising areas for further study.…”

“…(Precision* Recall) (Precision + Recall)(5) Where, 𝑇𝑟𝑃 = True Positive (condition when both actual and predicted values are of defective quality). 𝑇𝑟𝑁 = True Negative (Condition when both actual and predicted values are of good quality).…”

A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

“…A SLR should be supported by figures and tables and made visually understandable. In this section, selected studies are analyzed and classified from different perspectives [37][38][39][40]. The classifications made are shown with graphics and supported by numerical results.…”

Benchmarking Maintenance Practices for Allocating Features Affecting Hydraulic System Maintenance: A West-Balkan Perspective

“…Nevertheless, entering the digitalisation and cloud-computing era, many companies have started to rely on Big Data and Visual Analytics [23] to assess business issues by utilising large-scale multivariate data analysis (MDA) [24]. MDA's benefits include its ability to incorporate large-scale heterogenous data-unstructured text, categorical data, numerical data, logs, binary data-and project it in a lower-dimensional subspace, which is necessary to be able to investigate the latent indicators [25] impacting operational performance.…”

“…This is primarily the case in asset-intensive industries with heavyduty machinery, such as hydraulic power systems, where maintenance research is concerned mainly with diagnostic and prognostic aspects [22], thus needing more evidence regarding the impact of latent factors on MPIs. (Note: See Abbreviation) Nevertheless, entering the digitalisation and cloud-computing era, many companies have started to rely on Big Data and Visual Analytics [23] to assess business issues by utilising large-scale multivariate data analysis (MDA) [24]. MDA's benefits include its ability to incorporate large-scale heterogenous data-unstructured text, categorical data, numerical data, logs, binary data-and project it in a lower-dimensional subspace, which is necessary to be able to investigate the latent indicators [25] impacting operational performance.…”