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Abstract：
This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.
摘要：
本研究介绍了一种使用解释比（ER）线性判别分析（LDA）对多级离心泵（MCP）进行故障诊断的创新方法。最初，该方法通过识别故障敏感频段（FSFB）来解决振动信号中背景噪声和干扰的挑战。从FSFB，及时提取原始混合统计特征，频率,和时频域，形成一个全面的功能池。认识到并非所有特征都能充分代表MCP条件，并且会降低分类准确性，我们提出了一种新的ER-LDA方法。ER-LDA通过计算类间距离和类内散射之间的解释比率来评估特征重要性，通过LDA促进判别特征的选择。基于ER的特征评估和LDA的这种融合产生了新颖的ER-LDA技术。然后，将得到的选择性特征集传递给k-最近邻（K-NN）算法进行条件分类，区分正常，机械密封孔，机械密封划痕,以及MCP的叶轮缺陷状态。所提出的技术在故障分类方面超越了当前的尖端技术。
