Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learning-based approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a deterministic value and hence cannot convey uncertainty in the RUL prediction. This practice usually provides overly confident predictions that might cause severe consequences in safety-critical industries. To address this issue, this paper proposes a probabilistic Bayesian recurrent neural network (RNN) for RUL prognostics considering epistemic and aleatory uncertainties. The epistemic uncertainty is handled by Bayesian RNN layers as extensions from the Frequentist RNN layers using the Flipout method. The aleatory uncertainty is covered by a probabilistic output that follows a Gaussian distribution parameterized by the two neurons in the output layer. The network is trained using Bayes by backprop with the Flipout method. The proposed model is demonstrated by the open-access Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engines and a comparative study of the Frequentist RNN counterparts, the Monte Carlo Dropout-based RNN, and the state-of-the-art models for C-MAPSS datasets. The results demonstrate the promising performance and robustness of the proposed model in RUL prognostics.

中文翻译：

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考虑认知和随机不确定性的用于剩余使用寿命预测的概率贝叶斯递归神经网络

基于深度学习的方法已成为处理来自复杂物理资产中多传感器套件的大型机械数据并预测其剩余使用寿命 (RUL) 的有前途的解决方案。然而，最近的基于深度学习的方法提供了 RUL 的单点估计，因为这些模型将神经网络的权重表示为确定性值，因此无法传达 RUL 预测中的不确定性。这种做法通常会提供过于自信的预测，可能会对安全关键行业造成严重后果。为了解决这个问题，本文提出了一种概率贝叶斯循环神经网络 (RNN)，用于考虑认知和随机不确定性的 RUL 预测。认知不确定性由贝叶斯 RNN 层处理，作为频率派 RNN 层的扩展，使用 Flipout 方法。偶然的不确定性被概率输出覆盖，该输出遵循由输出层中的两个神经元参数化的高斯分布。该网络使用贝叶斯通过反向传播和 Flipout 方法进行训练。所提出的模型通过涡轮风扇发动机的开放访问商业模块化航空推进系统仿真 (C-MAPSS) 数据集以及对频率派 RNN 对应物、基于蒙特卡罗辍学的 RNN 和状态的比较研究来证明 - C-MAPSS 数据集的最先进模型。结果证明了所提出的模型在 RUL 预测中的有希望的性能和稳健性。所提出的模型通过涡轮风扇发动机的开放访问商业模块化航空推进系统仿真 (C-MAPSS) 数据集以及对频率派 RNN 对应物、基于蒙特卡罗辍学的 RNN 和状态的比较研究来证明 - C-MAPSS 数据集的最先进模型。结果证明了所提出的模型在 RUL 预测中的有希望的性能和稳健性。所提出的模型通过涡轮风扇发动机的开放访问商业模块化航空推进系统仿真 (C-MAPSS) 数据集以及对频率派 RNN 对应物、基于蒙特卡罗辍学的 RNN 和状态的比较研究来证明 - C-MAPSS 数据集的最先进模型。结果证明了所提出的模型在 RUL 预测中的有希望的性能和稳健性。