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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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Data augmentation using a generative adversarial network for a high-precision instantaneous microwave frequency measurement system
In this Letter, an unsupervised-learning platform—generative adversarial network (GAN)—is proposed for experimental data augmentation in a deep-learning assisted photonic-based instantaneous microwave frequency measurement (IFM) system. Only 75 sets of experimental data are required and the GAN can augment the small amount of data into 5000 sets of data for training the deep learning model. Furthermore, frequency measurement error of the estimated frequency has improved by an order of magnitude from 50 MHz to 5 MHz. The proposed use of GAN effectively reduces the amount of experimental data needed by 98.75% and reduces measurement error by 10 times.
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- Award ID(s):
- 1653525
- PAR ID:
- 10373008
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Letters
- Volume:
- 47
- Issue:
- 20
- ISSN:
- 0146-9592; OPLEDP
- Format(s):
- Medium: X Size: Article No. 5276
- Size(s):
- Article No. 5276
- Sponsoring Org:
- National Science Foundation
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