A Study on Prediction Model of Equipment Failure Through Analysis of Big Data Based on RHadoop

With the development of the internet of things, which is widely applied not only to everyday objects but also to industrial areas, the production of big data is accelerating. To provide intelligent services without human intervention in the internet of things environment, intelligent communication between objects becomes the key, and since the failure of the mechanical equipment attached to the sensor causes malfunction of the object and product failure, big data analysis to predict equipment failure is becoming more important. The purpose of this study is to propose a model for predicting mechanical equipment failure from various sense data collected in the manufacturing process. This study constructed a RHadoop-based big data platform to distribute a large amount of datasets for research, and performed logistic regression modeling to predict the main variables causing the failure from various collected variables. As a result of the study, the main variables in the manufacturing process that cause equipment failure were derived from the collected sense data, and the fitness and performance evaluation for the prediction model were made using the ROC curve. As a result of the performance evaluation of the prediction model, the ROC curve showed a fairly high prediction accuracy with AUC close to 1. The results of this study are expected to be applicable to the prediction of malfunctions, product failure, or abnormal communication between objects due to miscellaneous product faults in our daily lives in the internet of things environment.