Development of Model-Based Artificial Intelligence Technology for Rotating Machinery Diagnosis

Abstract

Since rotating machineries are used and play crucial roles in various industries, it is important to diagnose condition of the machineries appropriately based on the knowledge of abnormal condition of the equipment. However, it is quite difficult to obtain the knowledge of abnormal condition. For this reason, simulation is expected to be an effective approach to estimate conditions of equipment instead of data acquisition. In this study, we focus on a diagnostic system that combines simulation and Artificial Intelligence. Simulating behavior of the rotating machinery including abnormal conditions in advance and using the result as training data, we construct a learning model that predicts the parameters of the simulation model from the measurement data. We consider a rotor shaft system and verify this method by predicting the weight as the amount of rotor unbalance through the learning model from the experimental measurement data.