Research Article
Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest
@INPROCEEDINGS{10.1007/978-3-030-32388-2_21, author={Tong Li and Chunhe Song and Yang Liu and Zhongfeng Wang and Shimao Yu and Shanting Su}, title={Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest}, proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings}, proceedings_a={MLICOM}, year={2019}, month={10}, keywords={Sparse auto-encoder Distributed fault diagnosis Fault classification Random forest}, doi={10.1007/978-3-030-32388-2_21} }- Tong Li
Chunhe Song
Yang Liu
Zhongfeng Wang
Shimao Yu
Shanting Su
Year: 2019
Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest
MLICOM
Springer
DOI: 10.1007/978-3-030-32388-2_21
Abstract
For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type.
