Proposal of failure prediction method of factory equipment by vibration data with Recurrent Autoencoder

Abstract

In this paper, we propose a method to predict the failure of factory equipment by machine learning architectures using vibration data. We design the model so that we can predict robustly the failure of the equipment in advance. We use a Gaussian Mixture Model (GMM), a machine learning architecture, to calculate abnormality value which is used for the decision whether the state of the equipment is normal or abnormal by thresholding. We also use Long Short-Term Memory Autoencoder (LSTM-AE), one of the structures of the deep learning algorithm, for feature extraction. LSTM-AE model learns both spatial and temporal patterns which are difficult to capture with conventional machine learning algorithms. We conducted the prediction experiment using vibration data obtained from actual mechanical equipment, to confirm our method can predict the failure more robust than conventional methods. From this experiment, we found that the abnormality value tended to exceed a threshold value before the actual failure, indicating that the failure can be predicted in advance by our method. Besides, when compared with conventional methods, we found that the transition of abnormality and the accuracy of failure prediction were almost the same in all cases, but we also showed that the proposed method has superiority on robustness compared to conventional methods about the transition of abnormality and the setting of the threshold.

Keywords

• anomaly detection
• failure prediction
• factory equipment
• signal processing
• vibration
• machine learning
• deep learning
• autoencoder
• recurrent neural network
• gmm