Abstract
Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.
Highlights
Modern machinery is developing in the direction of high speed, precision, and efficiency
For the purpose of fault diagnosis on small sample data sets, the adaptive transfer density peak search clustering (ATDPS) algorithm was proposed in this paper. e transfer learning was applied to the DPS algorithm to improve the classification accuracy and reduce the computational time consumption. e results of the source domain are transferred to the classification of data in the target domain with only a small amount of data according to the data distribution and the best classification pattern. e main contributions of the intelligent mechanical fault diagnosis algorithm in this paper are summarized as follows: (1) A feature weight analysis algorithm is proposed to improve the classification accuracy of the DPS algorithm combined with wavelet packet transform energy feature analysis
For the problem of fault diagnosis of unlabelled data in practical applications, this paper proposed an intelligent fault diagnosis algorithm for rotating machinery based on ATDPS
Summary
Modern machinery is developing in the direction of high speed, precision, and efficiency. In the study of diagnosing for a small amount of unlabelled mixed data, transfer learning recently has been applied in the fields of mechanical fault diagnosis by researchers [6]. Shock and Vibration et al proposed a method based on deep transfer autoencoder for intelligent diagnosis of bearing faults among different mechanical equipment [8]. In the research of source domain sample classification, this paper uses an improved algorithm based on the fast density peak search clustering algorithm. For the purpose of fault diagnosis on small sample data sets, the adaptive transfer density peak search clustering (ATDPS) algorithm was proposed in this paper. (2) An adaptive transfer density peak search clustering algorithm (ATDPS) is developed to adjust the parameters for the classification of sample data adaptively.
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