Abstract

This paper introduces a new deep learning model for Remaining Useful Life (RUL) prediction of complex industrial system components using Gaussian Mixture Models (GMMs). The used model is an enhanced deep LSTM approach, for which Gaussian mixture clustering is performed for all collected sensors data and operational monitoring information. This distribution-based clustering using the hyperparameter ε leads to an adequate deep neural network for RUL prediction. An expectation-maximization algorithm was implemented to configure the deep LSTM network for RUL estimation. The proposed Gaussian mixture Clustering-based deep LSTM model for useful life prediction of the industrial components is trained and tested on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets. The experiments of the enhanced deep LSTM model show clearly the relevance of using Gaussian mixture clustering for quality improvement of RUL prediction through deep LSTM models. (https://github.com/sayahmhgithub/EnhancedLSTM4RUL.git).

中文翻译：

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使用高斯混合模型对RUL进行预测的深LSTM增强

摘要

本文介绍了一种新的深度学习模型，用于使用高斯混合模型（GMM）预测复杂工业系统组件的剩余使用寿命（RUL）。使用的模型是增强的深度LSTM方法，针对所有收集的传感器数据和操作监控信息执行高斯混合聚类。使用超参数ε的基于分布的聚类可为RUL预测提供足够的深度神经网络。实施了期望最大化算法，以配置用于LUL估计的深度LSTM网络。在商业模块化航空推进系统仿真（C-MAPSS）数据集上对建议的基于高斯混合聚类的深LSTM模型（用于预测工业组件的使用寿命）进行了培训和测试。增强的深LSTM模型的实验清楚地表明了使用高斯混合聚类通过深LSTM模型改善RUL预测质量的相关性。（https://github.com/sayahmhgithub/EnhancedLSTM4RUL.git）。