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, ۲-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 ۹۸.۶% for all data sets and ۹۴.۶% for the validation dataset. The accuracy of the ۲-layer FFNN is ۸۵% for all datasets and ۷۴.۲% for the validation dataset and the accuracy of the deep FFNN is ۸۲% for all datasets and ۶۷% for the validation dataset. Therefore, the developed CNN-based method has better performance than the FFNN-based models.

کلیدواژه ها:

fault detection ، rolling-element bearing ، Convolutional neural network ، Feed-forward neural network ، impact detection