An On-line Fault Diagnosis Method for Gas Engine Using AP Clustering

2017 ◽  
Author(s):  
Li Li ◽  
Zhaoming Wu
Keyword(s):  
Fault Diagnosis ◽  
Gas Engine ◽  
Diagnosis Method ◽  
On Line ◽  
Ap Clustering

Remote Fault Diagnosis System for Marine Power Machinery System

2013 ◽  
pp. 292-311
Author(s):  
Chengqing Yuan ◽  
Xinping Yan ◽  
Zhixiong Li ◽  
Yuelei Zhang ◽  
Chenxing Sheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Heavy Load ◽  
Diagnosis System ◽  
Power Machinery ◽  
Diagnosis Method ◽  
On Line ◽  
Identification Approach ◽  
Fault Diagnosis System ◽  
Machinery System ◽  
Remote Fault Diagnosis

Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera. Therefore, the failures in marine power machinery would badly threaten the safety of voyages. Keeping marine power machineries running reliably is the guarantee of voyage safety. For the condition monitoring and fault diagnosis of marine power machinery system, this study established the systemic condition identification approach for the tribo-system of marine power machinery and developed integrated diagnosis method by combining on-line and off-line ways for marine power machinery. Lastly, the remote fault diagnosis system was developed for practical application in marine power machinery, which consists of monitoring system in the ship, diagnosis system in laboratory centre, and maintenance management & maintenance decision support system.

Remote Fault Diagnosis System for Marine Power Machinery System

Data Mining ◽  
2013 ◽  
pp. 2174-2192
Author(s):  
Chengqing Yuan ◽  
Xinping Yan ◽  
Zhixiong Li ◽  
Yuelei Zhang ◽  
Chenxing Sheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Heavy Load ◽  
Diagnosis System ◽  
Power Machinery ◽  
Diagnosis Method ◽  
On Line ◽  
Identification Approach ◽  
Fault Diagnosis System ◽  
Machinery System ◽  
Remote Fault Diagnosis

Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera. Therefore, the failures in marine power machinery would badly threaten the safety of voyages. Keeping marine power machineries running reliably is the guarantee of voyage safety. For the condition monitoring and fault diagnosis of marine power machinery system, this study established the systemic condition identification approach for the tribo-system of marine power machinery and developed integrated diagnosis method by combining on-line and off-line ways for marine power machinery. Lastly, the remote fault diagnosis system was developed for practical application in marine power machinery, which consists of monitoring system in the ship, diagnosis system in laboratory centre, and maintenance management & maintenance decision support system.

On-line hybrid fault diagnosis method for high voltage circuit breaker

2017 ◽  
Vol 33 (5) ◽  
pp. 2763-2774 ◽  
Author(s):  
Mei Fei ◽  
Pan Yi ◽  
Zhu Kedong ◽  
Zheng Jianyong
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Circuit Breaker ◽  
High Voltage Circuit Breaker ◽  
Diagnosis Method ◽  
On Line

The Fault Diagnosis Method Based on Wavelet Packet Characteristic Entropy and Relevance Vector Machine

2012 ◽  
Vol 151 ◽  
pp. 3-6
Author(s):  
Shu Yan Zhao ◽  
Qi Wang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Relevance Vector Machine ◽  
Engine Test ◽  
Test Bed ◽  
Line Feature ◽  
Optimum Parameters ◽  
Diagnosis Method ◽  
On Line ◽  
Working Status

The sensor fault diagnosis method based on wavelet packet characteristic entropy and relevance vector machine are researched, which is used on engine test bed . In detail, the wavelet packet characteristic entropy is applied for feature extraction to get the feature matrix which denote all kinds of known working status of sensor, and the feature matrix are encoded; The feature matrix as inputs and feature codes as outputs are proposed for training the relevance vector machine classifier to get the optimum parameters. In the fault diagnosis unit, it uses the wavelet packet characteristic entropy to acquire the on-line feature matrix of sensor. The on-line feature matrix is supplied to the trained relevance vector machine classifier as inputs to validate the working status of sensor. An application of the method in engine test bed system is introduced. Finally, the applicability and effectiveness of the method is illustrated by experiments

scholarly journals On-Line Fault Diagnosis Method for Power Transformer Based on Missing Data Repair

2019 ◽  
Vol 472 ◽  
pp. 012027
Author(s):  
Xiansi Lou ◽  
Weihan Liao ◽  
Jianbo Xin ◽  
Qiukuan Zhou ◽  
Chen Kang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Missing Data ◽  
Power Transformer ◽  
Diagnosis Method ◽  
On Line
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