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.

人工智能（AI）和预测性维护（PdM）使用IIoT数据并通过估计剩余使用寿命（RUL）来实现零维护停机时间，从而在工业中进行维护。大多数已报告的工作都考虑使用相同数量的有关不同机器健康状况的正常样本和故障样本来提供训练数据。但是，实际方案必须处理故障数据的不可用性，从而导致训练数据集不平衡。在RUL估计方法中，此问题可能会导致错误预测遗漏。本文提出了一种基于条件生成对抗网络（CGAN）和深度门控递归单元（DGRU）网络的新型预后框架。该框架可以生成多变量故障实例，解决数据不平衡问题，并以最小的延迟预测复杂系统的RUL。