A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

Abstract

To support the mission of providing safe electricity generation with a high capacity factor, a Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to optimize component maintenance operations is developed. Using sensor measurements to better predict the true component’s Remaining Useful Life (RUL), the PdM framework has the potential to optimize maintenance costs by performing maintenance only when necessary. The PdM framework to pump bearings, the framework consists of a Convolutional Neural Network Autoencoder (CNN-AE) to detect component deviations from normality, a CNN to characterize component fault modes, and a Bayesian Neural Network (BNN) to estimate the component RUL with uncertainty. To increase the number of training samples, a synthetic data generation procedure was developed and includes procedures to recreate the fault-specific characteristic frequencies for diagnostics and the degradation trends for prognostics. The MLMs trained on the synthetic data are tested on the Center for Intelligent Maintenance Systems (IMS) dataset to showcase how well the synthetic data replicates measurement data. Utilizing this framework, the PdM was found to delay maintenance on average by a total of 8.92 years over 40 years and decrease the unexpected component failure rate from 10% to 0% when compared to traditional maintenance philosophies.