DT-CNN based Motor Failure Prediction Considering Outlier Data

One of the major problems with the existing motor failure prediction system is to assume that all motors with the same fault condition have the same or a similar signal. This is a problem that arises because it is impossible to measure all the countless types of motors and data of driving conditions and failures. It is difficult to implement a general-purpose failure prediction system with an existing system having limited data and limited output. Data that have a large difference because they do not exist in the existing system are called outlier data. In previous studies, the problem arising from the outlier data has not been considered. To solve this problem, a system designed by separating the failure diagnosis model and the failure prediction model is proposed. The diagnostic model of the proposed system can detect data that are not inside big data using a decision-tree convolution neural network (DT-CNN). By using the diagnostic model and the predictive model in series, it is possible to analyze data in a non-measured state more efficiently. Additionally, a method for averaging the outputs of the diagnostic and predictive models is proposed. Through this, the deep learning algorithm can obtain in effect of applying the filter. Furthermore, the average values can be used to confirm the long-term signal change trend. The proposed system improves the problems of the existing failure prediction and enables more practical failure prediction.