图 书 馆
深度学习与生存分析相结合的资产健康管理框架A Framework of Combining Deep Learning and Survival Analysis for Asset Health Management |
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| 课程网址: | https://videolectures.net/videos/kdd2016_liao_health_management |
| 主讲教师: | Linxia Liao |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a method to integrate feature extraction and prediction as a single optimization task by stacking a threelayer 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. |
| 关 键 词: | 深度学习; 生存分析; 管理框架 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-11：liyq |
| 最后编审: | 2025-03-11：liyq |
| 阅读次数: | 3 |