Simulation for predictive maintenance using weighted training algorithms in machine learning

Abstract

<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

Contribution of artificial intelligence to industrial maintenance in the field of mechanics

“…Artificial intelligence offers tools that are completely decoupled from the structure of the system, not requiring prior modeling of the latter and allowing real-time monitoring of its evolution, and its tools use several algorithms, among others, those applied for maintenance [22].…”

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

“…Based on the evidence from Figure 1, MTBF (Main Time Between Failure) is the most popular KPI in the field of asset valuation due to its easy accessibility and understanding (Jittawiriyanukoon and Srisarkun, 2022). In addition, asset performance monitoring is of great use in predicting any potential asset behavior, problems and losses (Márquez et al ., 2019), followed by asset reliability, availability and efficiency with the same objective (Liu et al ., 2022; Ruschel et al ., 2020; Yu et al ., 2019).…”

Comparison of various data mining methods for early diagnosis of human cardiology

“…Ghosh et al [17] used dataset Wisconsin breast cancer database (WBCD) to introduced a novel approach by using severial algorithm like LR, DT, RF, SVM and KNN and used Least Absolute Shrinkage and Selection Operator (LASSO) as feature selction and result show RF was best accurate by 99.41%; Jittawiriyanukoon and Srisarkun [18]. They used location data, machines, statistics, and downtime from a data-mining plant using artificial intelligence and machine learning to develop a decision support strategy; scheduling a maintenance plan by using open source software for replacing the shortcut of maintenance planning and scheduling; on data mining, 3 promising training algorithms are used for insightful data as a result precision numbers have been obtained; Alalwan et al [19] two data mining algorithms were used a self-organizing map and a RF for diabetes diagnosis; the results have shown that they can provide services in the health care sector to make effective decisions; Saranya and Pravin [20] in this work, they comprehensively study the different strategies used to predict diseases by applying several mining algorithms to improve prediction to reduce hospital admissions and reduce patient costs.…”