液压泵特征提取是实现故障预测的关键环节。针对液压泵退化特征不理想的问题，提出一种基于改进复合谱与关联熵融合的特征提取方法。首先，对传统CS算法进行改进，对多通道振动信号进行融合，实现对特征信息的综合利用，并分别提取Shannon定义下和Tsallis定义下的DCS功率谱熵和DCS奇异熵作为特征；在此基础上，提出基于关联熵的融合方法，将所提取的特征融合为一个全新特征，作为液压泵退化特征，提高特征的简洁度；最后，利用液压泵性能退化试验所采集振动信号，验证了该方法的有效性。
An feature extraction of the key step in prognostic of hydraulic pump. Since vibration signals of hydraulic pump are complex and degradation features are hard to extract, a novel method based upon DCS and relation entropy is proposed. First of all, in order to make reasonable use of feature information, earlier CS is modified by DCT and the DCS algorithm is presented to make fusion of multi-channel vibration signals. And DCS power entropy and singular entropy, which are relatively defined in Shannon entropy and Tsallis entropy, are extracted as features. On this basement, the feature fusion method based on relation entropy is proposed to remain original features performances and improve conciseness. According to max relation entropy criterion and gradual fusion strategy, the four extracted features are fused into a new one, which is considered as degradation feature. Finally, the proposed method is verified by vibration signals sampled from hydraulic pump degradation experiment.
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