Data Mining On Machine Breakdowns And Effectiveness Of Scheduled Maintenance - Tan, Su Sze (Universiti Sains Malaysia, 2019)
In computer-integrated manufacturing, machines are equipped with sensors and memory storage. This provides a large corpus of information retrievable for data mining and knowledge discovery. The case study is focused on the investigation of machine breakdown and the effectiveness of scheduled maintenance with the application of data mining. The research methodology involves eight steps. The initial step is performed business understanding to gain insight of data mining on machine performances. Several questions in the stage of macro-level and micro-level are generated. Second, a proposed simulation model for the operational process was designed based on a real medical tool manufacturing plant. The production is a job shop whereby products in batch have to go through a number of processes and multistations. Each process alters particular attributes of the product. The production simulation would be constructed in Witness Horizon V21. Six different machine breakdown scenarios were modelled. Different feature processing strategies would be devised, in particular time-related data. Third, a relational database is developed to store the information from the simulation. The next step is involved data preprocessing which includes data selection, data cleaning and data transformation. Data mining is the sixth step in which software of Orange is used as the tool. Seventh, the pattern evaluation is developed to present the discovery of data which helps in decision-making. From the research, it is found that there have ten types of breakdowns affecting the performance of machine and the breakdown of coolant leaking is the main contributor as compared to others breakdown. Besides, the frequency of breakdown especially for coolant leaking has decreased after the maintenance is scheduled on the machine. Hence, the application of maintenance on machine is effective in controlling the frequency of breakdown. From the results of data, it is able to evaluate the breakdown of machine and support the decision on scheduling maintenance on machine. However, it requires a large amount of cost to be invested in maintenance. Last but not least, some of the complex data mining tasks are not able to perform because of the limited algorithms and machine learning in Orange software.