Combining Deep Learning and Survival Analysis for Asset Health Management

Linxia Liao and Hyung-il Ahn
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Special Issue Big Data and Analytics
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Full Paper
ijphm_16_020.pdf392.73 KBSeptember 6, 2016 - 7:51am

We propose a method to integrate feature extraction and prediction as a single optimization task by stacking a three-layer model as a deep learning structure. The first layer of the deep structure is a Long Short Term Memory (LSTM) model which deals with the sequential input data from a group of assets. The output of the LSTM model is followed by mean-pooling, and the result is fed to the second layer. The second layer is a neural network layer, which further learns the feature representation. The output of the second layer is connected to a survival model as the third layer for predicting asset health condition. The parameters of the three-layer model are optimized together via stochastic gradient decent. The proposed method was tested on a small dataset collected from a fleet of mining haul trucks. The model resulted in the ``individualized'' failure probability representation for assessing the health condition of each individual asset, which well separates the in-service and failed trucks. The proposed method was also tested on a large open source hard drive dataset, and it showed promising result.

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Submission Keywords: 
deep learning; long short term memory; survival analysis; prognostics and health management
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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