Data driven predictive analytics for a spindle´s health

Prediction of a spindle´s health is of critical significance in a manufacturing environment. Unexpected breakdowns in a spindles functioning can lead to high costs and production delays. Therefore, developing methods which can predict the time-to-failure of a spindle and its bearings can be of significant importance. One of the main challenges for successful prediction by a purely data-driven techniques is the management and analysis of a huge volume of spindle´s monitored operational data. In this paper, we build a regression and clustering based prediction methodology, suitable for exploiting very high volumes of monitored data, for a spindle´s time-to-failure prediction. We conquer the problem of dealing with huge volumes of monitored data by aggregating features related to spindle vibration acceleration in the frequency domain for 24-72 hour windows of time. A Fast Fourier Transform analysis of the spindle vibration and angular acceleration data is used to extract twelve aggregated frequency domain features to train regression models. Further, a graph clustering algorithm is used to improve the feature selection and thus the accuracy of the predictions. Once the model is trained, it can be used to make predictions based upon energy bursts observed over 24-72 hour windows. This can prove to be very useful as a cost effective prognostic tool that can be easily deployed in the manufacturing industry. The spindle data used in the paper has been collected at Techsolve Inc. over three run-to-failure experiments performed on a spindle test-bed. In this paper, we present our model-building methodology along with an experimental setup and empirical evaluation of the prediction models and results.