Fault detection of rolling element bearing using a temporal signal with artificial intelligence techniques

Fault detection of rolling element bearing (REB), has a very effective role in increasing the reliability of machinery and improving future decisions for rotating machinery operation. In this study, a new method based on a convolutional neural network (CNN) is developed for fault detection of REB. Its performance will be compared with other artificial intelligence (AI) techniques, 2-layer, and deep feedforward neural network (FFNN). In this regard, a set of accelerated-life tests has been implemented on an experimental platform. The models are aimed to recognize the impact pattern in the raw signals generated by faulty REBs. The innovation of the present study is to convert the high-dimensional input as a raw temporal signal to low-dimensional output. The developed method does not need preprocessing of data. Using several types of accelerated tests prevents overfitting. The result shows that the accuracy of the developed CNN-based method is 98.6% for all data sets and 94.6% for the validation dataset. The accuracy of the 2-layer FFNN is 85% for all datasets and 74.2% for the validation dataset and the accuracy of the deep FFNN is 82% for all datasets and 67% for the validation dataset. Therefore, the developed CNN-based method has better performance than the FFNN-based models.