Abstract
Effective and efficient diagnosis methods are highly demanded to improve system reliability. Comparing with conventional fault diagnosis methods taking a forward approach (e.g., feature extraction, feature selection, and fusion, and then fault diagnosis), this paper presents a new association rule mining method which provides an inverse approach unearthing the underlying relation between labeled defects and extracted features for bearing fault analysis. Instead of evenly dividing methods used in traditional association rule mining, a new association rule mining approach based on the equal probability discretization method is presented in this study. First, a series of extracted features of signal data are discretized following the guideline of equalized probability distribution of the data in order to avoid excessive concentration or decentralized data. Next, the data matrix composed of arrays of discretized features and defect labels is exploited to generate the association rules representing the relation between the features and fault types. Experimental study on a bearing test reveals that the proposed method can generate a series of underlying association rules for bearing fault diagnosis, and the related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification. As a new feature selection method, it possesses prominent superiority compared to traditional PCA, KPCA, and LLE dimension reduction methods.
Highlights
Effective and efficient diagnosis methods are highly demanded to improve system reliability
As a new feature selection method, it is different from other data dimensionality reduction methods, such as principal component analysis (PCA), kernel principal component analysis (KPCA), and locally linear embedding (LLE), which need to transform the data matrix leading to bereave of the actual physical meaning of the selected features. e related features selected by the proposed method can be used directly to analyze the fault to avoid the impact of irrelevant features on the premise of keeping the original feature state. e range of eigenvalues obtained from the representative features can be used to determine the type and size of fault according to the respective values of features
To improve the reliability of rotary machinery, effective and efficient diagnosis methods are highly needed. ere is a new equal probability-based association rule mining method presented in this paper which provides an approach directly unearthing the underlying relation between labeled defects and unusual features for bearing fault analysis
Summary
Many studies published in the literature adopt association rule mining to find useful knowledge from database proactively. e discovered knowledge with association rules can be applied to information management, decision making, process control, and many other applications. Association rule mining is a technique to detect and extract meaningful association relationships hidden in databases. It is firstly introduced by Agrawal et al [34] and has been investigated in different applications including commodity sales [35], disease study [36], quality improvement of a production process [37], and alarm correlation analysis [38]. En each large itemset is used to generate the desired rules, which satisfies the minimum confidence constraint. Is seed set is used to generate new potentially large itemsets, called candidate itemsets. E Apriori algorithm generates the candidate itemsets without considering the transactions in the database to improve the computational efficiency At the end of the pass, the actual large candidate itemsets become the seed for the pass. is process continues until no new frequent itemsets are found. e Apriori algorithm generates the candidate itemsets without considering the transactions in the database to improve the computational efficiency
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