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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 17; p. 6321
• Main Authors: Cheng, Xiang, Chaw, Jun Kit, Goh, Kam Meng, Ting, Tin Tin, Sahrani, Shafrida, Ahmad, Mohammad Nazir, Abdul Kadir, Rabiah, Ang, Mei Choo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.08.2022; MDPI
• Subjects: Artificial Intelligence; Decision making; deep learning; Electric power production; Energy consumption; Energy efficiency; explainable artificial intelligence; Factories; Humans; industry 4.0; Information management; Literature reviews; Machine learning; Manufacturing; Manufacturing Industry; Mathematical programming; Optimization; predictive maintenance (PdM); Sensors; Subject specialists; Systematic Review; visual analytics; Visualization; Malaysia; explainable artificial intelligence; visual analytics; deep learning; machine learning; industry 4.0; predictive maintenance (PdM)
• DOI: 10.3390/s22176321
• ISSN: 1424-8220

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.