Fault Diagnosis of Reciprocating Compressor Using Component Estimating Empirical Mode Decomposition and De-Dimension Template With Double-Loop Correction Algorithm
Fault Diagnosis of Reciprocating Compressor Using Component Estimating Empirical Mode Decomposition and De-Dimension Template With Double-Loop Correction Algorithm
复制标题
采用分量估计经验模态分解和双环修正算法去量纲模板进行往复式压缩机故障诊断
DOI：
10.1109/access.2019.2925836
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Guo Qinghua
中科院分区：
文献类型：
--
作者：
Wang Hongyi;Dong Feng;Zhou Xinxiu;Wang Hongyu;Zhu Xinjun;Song Limei;Guo Qinghua
This paper presents an approach to implement multi-parameter (i.e., pressure, temperature, vibration, current, and liquid level) signals for fault diagnosis of the reciprocating compressor (RC). Due to the complexity of structure and motion of such compressor, the acquired signals involve transient impacts and noises. This causes the useful information to be corrupted and makes it difficult to diagnose the fault patterns accurately. A component estimating empirical mode decomposition (CEEMD) method is proposed to remove the random noise and improve data quality. Furthermore, a new template matching algorithm called de-dimension template with double-loop correction (DDT-DLC) is applied to diagnose the fault pattern contained in the time series signals. The DDT employs a judging criterion for key characterization parameters extraction and a multicellular parameter fusion method to reduce the dimension of the matching template, and then, the DLC supplies a double-loop correction algorithm to build a parameter state array computing model of the time series data by adjusting the dynamic factors. The proposed approach is validated with three fault patterns and the healthy pattern in a two-stage reciprocating air compressor. To confirm the superiority of the proposed method, its performance is compared with that of the traditional methods. The results have indicated that the proposed approach is of highly diagnostic accuracy and shortly computing time in the fault diagnosis.
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DOI：
10.1109/bigdataservice.2016.27
发表时间：
2016-03
期刊：
2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService)
影响因子：
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作者：
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DOI：
--
发表时间：
2004-09
期刊：
2004 12th European Signal Processing Conference
影响因子：
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通讯作者：
P. Flandrin;P. Gonçalves;G. Rilling
影响因子：
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