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It is crucial to effectively and accurately diagnose fault of rotating machinery. However, high dimension characteristic of features, which are extracted from vibration signals of Rotating machinery, makes it difficult to recognize accurately fault mode. To resolve this problem, t-distributed stochastic neighbor embedding (t-SNE) is introduced to reduce the dimensionality of the feature vector in this paper. Therefore, the article proposes a method for fault diagnosis of Rotating machinery based on local characteristic decomposition-sample entropy (LCD-SampEn), t-SNE and random forest (RF). Firstly, original vibration signals of rotating machinery are decomposed to a number of ISCs by the LCD. Then, feature vector is obtained through calculating SampEn of each ISC. Subsequently, the t-SNE is used to reduce the dimension of the feature vectors. Finally, the reconstructed feature vectors are applied to the RF for implementing the classification of fault patterns. Two cases are studied based on the experimental data of bearing and hydraulic pump fault diagnosis, in which the proposed method can achieve 98.22 % and 98.75 % of diagnosis rate respectively. Compared with the pear methods, the proposed approach exhibits the best performance. The results validate the effectiveness and superiority of the present method.

An approach to fault diagnosis for rotating machinery based on feature reconstruction with LCD and t-SNE