Generative adversarial networks based remaining useful life estimation for IIoT

Artificial intelligence (AI) and Predictive Maintenance (PdM) become productive using IIoT-data with zero-downtime for maintenance in industries by estimating the remaining useful life (RUL). Most reported works consider training data availability with an equal number of normal and fault samples concerning different machine health conditions. However, practical scenarios have to deal with fault-data unavailability, resulting in an imbalanced training dataset. This problem can lead to inaccuracies with missed fault-prediction in RUL estimation approaches. This paper proposes a novel prognostics framework based on conditional generative adversarial network (CGAN) and deep gated recurrent unit (DGRU) network. The framework can generate multi-variate fault instances, solve data imbalance, and predict the RUL of complex systems with the least latency. We observed that the learning of fault samples using underlying noise distribution, data augmentation, and training DGRU improves the RUL prediction accuracy by at least 15% compared to reported imbalanced work on the C-MAPSS dataset.

Automated summary

This paper proposes a novel prognostics framework based on CGAN and DGRU network, which can generate multi-variate fault instances, solve data imbalance, and predict the RUL of complex systems with the least latency. Experimental results show that by using data augmentation and training DGRU, the RUL prediction accuracy has improved by at least 15% compared to reported imbalanced work.