Machine learning and deep learning on health diagnosis of rotating fan

Abstract

ABSTRACT Machine learning and deep learning on the health diagnosis of a rotating machine are studied for smart monitoring. The signals of vibration and sound pressure of a rotating fan driven by DC motor detected by an accelerometer and microphone are processed by machine/deep learning for health diagnosis of blade. For the machine learning, two methods, support vector machine (SVM) and random forest (RF), are used for classification of normal and abnormal status based on three features extracted from the signals in time domain and frequency domain. For the deep learning, convolution neural network (CNN) method is used to process the two signals in time domain for modelling; certain layers of convolution and pooling for feature extraction are followed by two layers of artificial neural network. After the learning, a confusion matrix of testing is given to evaluate the performance. In particular, the importance scores of input features are analyzed by RF, which is useful for us to screen out the non-significant features for improving the learning to avoid overfitting. We demonstrate three different methods (SVM, RF, CNN) on the diagnosis of a rotating fan with a damaged blade to illustrate their characteristics.