Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion

Abstract

For accurately identifying the crack severity of turbine blades, a novel intelligent diagnosis framework is proposed in our paper, which uses multiscale sparse filtering (MSF)-based unsupervised sparse feature learning and multi-kernel support vector machine for information fusion (MKSVMIF). To realize the technical readiness and the state-of-the-art diagnostic performance, advanced signal processing methods are used to eliminate interference and retain fault-related characteristics. Thus, the EEMD-based multiwavelet packet energy entropy (EEMD-WPEE) as an enhanced method is proposed for multi-source three-dimensional blade tip clearance signals (3D-BTC), which is used to enhance the fault-related information. Afterward, the MSF is constructed to adaptively learn multisource sparse features from EEMD-WPEE representation of 3D-BTC by an unsupervised manner. Finally, the MKSVMIF is proposed to fuse these sparse features and diagnose crack severities. To validate the effectiveness of our proposed MSF-MKSVMIF framework, Extensive experiments are conducted on a blade-rotor simulation rig, and the results show that our proposed framework is superior to other comparison methods and can quantitatively detect different blade crack severities with a relatively small number of samples.