Convolutional Auto-Encoder Based Degradation Point Forecasting for Bearing Data Set

In smart manufacturing industry, health analysis and forecasting of degradation starting point has become an increasingly crucial research area. Prognostics-aware systems for health analysis aim to integrate health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Developments in smart manufacturing as well as deep learning-based prognostics provide new opportunities for health analysis and degradation starting point forecasting. Rotating machines have many critical components like spinning, drilling, rotating, etc. and they need to be forecasted for failure or degradation starting times. Moreover, bearings are the most important sub-components of rotating machines. In this study, a convolutional neural network is used for forecasting of degradation starting point of bearings by experimenting with Nasa Bearing Dataset. Although convolutional neural networks (CNNs) are utilized widely for 2D images, 1-dimensional convolutional filters may also be embedded in processing temporal data, such as time-series. In this work, we developed one such autoencoder network which consists of stacked convolutional layers as a contribution to the community. Besides, in evaluation of test results, L-10 bearing life criteria is used for threshold of degradation starting point. Tests are conducted for all bearings and results are shown in different figures. In the test results, proposed method is found to be effective in forecasting bearing degradation starting points.