Abstract
Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.
Highlights
As a type of equipment widely used in modern industry, rotating machinery is becoming more precise and with more complicated structures
We investigate the potential of introducing the dictionary learning scheme as the initial feature matrix extraction method to achieve improved sensitivity and diagnosis capability of singular values
In principle, increasing the atom length maps directly to the computational burden and reduces the learning capacity based on existing maps directly to the computational burden and reduces the learning capacity based on existing dictionary learning algorithms
Summary
As a type of equipment widely used in modern industry, rotating machinery is becoming more precise and with more complicated structures. Mechanical sub-systems in rotating machinery, especially the critical components such as bearings [1], gearbox [2], rotor [3] and fan [4]. Condition monitoring and fault diagnosis (CMFD) technology is a promising tool to realize early fault alarms and minimize losses. Among the various approaches used in CMFD technology, the signal-based diagnosis approaches and data-driven diagnosis approaches attract continuous interest [5,6]. In signal-based approaches, the foundation is that the fault information can be reflected in the monitored signals, and a diagnosis result can be made by checking the consistency between real-time data and healthy signal patterns
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