Failure prognostics is the process of predicting the remaining useful life (RUL) of machine components, which is vital for the predictive maintenance of industrial machinery. This paper presents a new deep learning approach for failure prognostics of rolling element bearings based on a Long Short-Term Memory (LSTM) predictor trained simultaneously within a Generative Adversarial Network (GAN) architecture. The LSTM predictor takes the current and past observations of a well-defined health index as an input, uses those to forecast the future degradation trajectory, and then derives the RUL. Our proposed approach has three unique features: (1) Defining the bearing failure threshold by adopting an International Organization of Standardization (ISO) standard, making the approach industry-relevant; (2) Employing a GAN-based data augmentation technique to improve the accuracy and robustness of RUL prediction in cases where the deep learning model has access to only a small amount of training data; (3) Integrating the training process of the LSTM predictor within the GAN architecture. A joint training approach is utilized to ensure that the LSTM predictor model learns both the original and artificially generated data to capture the degradation trajectories. We utilize a publicly available accelerated run-to-failure dataset of rolling element bearings to assess the performance of the proposed approach. Results of a five-fold cross-validation study show that the integration of the LSTM predictor with GAN helps to decrease the average RUL prediction error by 29% over a simple LSTM model without GAN implementation.
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An Adversarial Learning Approach for Machine Prognostic Health Management
Achieving accurate remaining useful life (RUL) prediction for prognostic and health management (PHM) depends upon sufficient prior degradation apprehension of critical components within the system. However, such prior knowledge is not always readily available in practice. We alleviate this shortcoming by proposing a novel data-driven framework that is capable of providing accurate RUL prediction without the need for any prior failure threshold knowledge. Correlative and monotonic metrics are utilized to identify critical features throughout the degradation progress. Subsequently, we append one-hot health state indicators to extracted degrading features, which are utilized together as adversarial training data for a Long Short-Term Memory (LSTM) network-based model. Finally, we utilize a fully connected layer to project the LSTM outputs into the parameters of a Gaussian mixture model (GMM) in conjunction with a categorical distribution, from which the long-term degradation progress is sampled. We verify the performance of the proposed framework using aeroengine health data simulated by Modular Aero-Propulsion System Simulation (MAPSS), and the results demonstrate that significant performance improvement can be achieved for long-term degradation progress and RUL prediction tasks.
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- Award ID(s):
- 1809164
- NSF-PAR ID:
- 10105853
- Date Published:
- Journal Name:
- 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)
- Page Range / eLocation ID:
- 163 to 168
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HPBDIS.2019.8735480
