Model-based health condition monitoring method for multi-cell series-connected battery pack

2016 ◽  
Author(s):  
Rui Xiong ◽  
Fengchun Sun ◽  
Hongwen He
Keyword(s):  
Condition Monitoring ◽  
Health Condition ◽  
Battery Pack ◽  
Monitoring Method ◽  
Model Based ◽  
Cell Series

A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 981-984
Author(s):  
Jian Guo Cui ◽  
Can Wu ◽  
Li Ying Jiang ◽  
Yi Wen Qi ◽  
Guo Qiang Li
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Probabilistic Neural Network ◽  
Complex Structure ◽  
Reference Value ◽  
Health Condition ◽  
Monitoring Method ◽  
Accurate Localization ◽  
Safety And Reliability ◽  
Path System

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

EMD, Ranking Mutual Information and PCA Based Condition Monitoring

2010 ◽  
Author(s):  
Xiaomin Zhao ◽  
Ming J. Zuo ◽  
Tejas Patel
Keyword(s):  
Mutual Information ◽  
Condition Monitoring ◽  
Feature Fusion ◽  
Health Condition ◽  
Multidimensional Analysis ◽  
Analysis Tool ◽  
Monitoring Method ◽  
Health Monitoring System ◽  
Monotonic Trend ◽  
Monitoring Parameter

Success of any health monitoring system chiefly relies on the effectiveness of condition monitoring parameter. The parameter could be a single or combination of many vibration features. These features are expected to have a monotonic trend with the damage/fault progression. Ranking mutual information technique has the ability to detect the features that have monotonic trend and PCA is a popular and widely accepted multidimensional analysis tool for the feature fusion. A condition monitoring method is presented in this paper by combining EMD, ranking mutual information and PCA. The proposed method is helpful in generation of the indicator that represents the damage progression. This method is tested on the impeller health condition monitoring of a pump.

Gearbox On-Line Condition Monitoring Using Empirical Mode Decomposition

2009 ◽  
Author(s):  
Qiang Miao ◽  
Dong Wang ◽  
Hong-Zhong Huang ◽  
Bin Zheng ◽  
Xianfeng Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Health Condition ◽  
Successful Implementation ◽  
Monitoring Method ◽  
Mode Decomposition ◽  
On Line ◽  
Strategy Condition ◽  
Engineering Practices ◽  
Actual Health

As a flexible maintenance strategy, Condition Based Maintenance (CBM) has been accepted by industry due to its efficiency and robustness in many engineering practices. Successful implementation of CBM relies on observation of actual health condition of machinery. Therefore, it is crucial to perform condition monitoring in CBM. This paper focuses on quantifying health condition of machinery. Empirical Mode Decomposition (EMD) is employed to decompose signal and extract dominant signatures, which could reflect health condition variation of machinery. Then, a novel index called Health Index (HI) is proposed to describe condition development trends. In order to detect occurrence of early faults, a dynamic threshold is also proposed. In case occurrence of early fault, HI should be higher than its corresponding threshold. This novel condition monitoring method is more appropriate for on-line health monitoring and detection of incipient fault. Two sets of data collected from gearboxes are used to validate the proposed method. The analysis results show that the proposed method is effective in condition monitoring, especially the detection of early faults.

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