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首页> 外文会议>IEEE International Conference on Prognostics and Health Management >Long Short-Term Memory Network for Remaining Useful Life estimation
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Long Short-Term Memory Network for Remaining Useful Life estimation

机译：长短期记忆网络，可用于估计剩余使用寿命

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摘要

Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a critical role in Prognostics and Health Management(PHM). Data driven approaches for RUL estimation use sensor data and operational data to estimate RUL. Traditional regression based approaches and recent Convolutional Neural Network (CNN) approach use features created from sliding windows to build models. However, sequence information is not fully considered in these approaches. Sequence learning models such as Hidden Markov Models (HMMs) and Recurrent Neural Networks (RNNs) have flaws when modeling sequence information. HMMs are limited to discrete hidden states and are known to have issues when modeling long-term dependencies in the data. RNNs also have issues with long-term dependencies. In this work, we propose a Long Short-Term Memory (LSTM) approach for RUL estimation, which can make full use of the sensor sequence information and expose hidden patterns within sensor data with multiple operating conditions, fault and degradation models. Extensive experiments using three widely adopted Prognostics and Health Management data sets show that LSTM for RUL estimation significantly outperforms traditional approaches for RUL estimation as well as Convolutional Neural Network (CNN).
机译：组件或系统的剩余使用寿命（RUL）定义为从当前时间到使用寿命结束的时间。准确的RUL估计在预测和健康管理（PHM）中起着至关重要的作用。 RUL估计的数据驱动方法使用传感器数据和操作数据来估计RUL。传统的基于回归的方法和最近的卷积神经网络（CNN）方法都使用从滑动窗口创建的功能来构建模型。但是，在这些方法中没有充分考虑序列信息。在对序列信息进行建模时，诸如隐马尔可夫模型（HMM）和递归神经网络（RNN）之类的序列学习模型存在缺陷。 HMM限于离散的隐藏状态，并且已知在对数据中的长期依赖性进行建模时会出现问题。 RNN也存在长期依赖性的问题。在这项工作中，我们提出了一种用于RUL估计的长短期记忆（LSTM）方法，该方法可以充分利用传感器序列信息，并通过多种操作条件，故障和降级模型来揭示传感器数据中的隐藏模式。使用三个广泛采用的Prognostics和Health Management数据集进行的广泛实验表明，用于RUL估计的LSTM明显优于传统的RUL估计方法以及卷积神经网络（CNN）。

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