Papers by Author: Yanyang Zi

Abstract: In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

Abstract: Wavelet transform is a powerful technique well suited to non-stationary signal processing. The properties of wavelet are determined by its basis function. In the fields of modal analysis, mechanical condition monitoring and fault diagnosis, impulse responses or transient responses are very common signals to be analyzed. The Laplace wavelet is a single-sided damped exponential wavelet and is a desirable wavelet basis to analyze signals of impulse response. A correlation filtering approach is introduced using the Laplace wavelet to identify the impulse response from vibration signals. Successful results are obtained in identifying the natural frequency of a hydro-generator shaft, and diagnosing the wear fault of intake valve of an internal combustion engine.

Abstract: An improved method to identify the crack location and size is presented which takes advantages of wavelet finite element (WFE). The important property of wavelet analysis is the capability to represent functions in a dynamic multiscale manner, so solution with WFE enables a hierarchical approximation to the exact solution. WFE has good ability in modal analysis for singularity problems like a cracked beam. The crack in a beam is modeled with WFE and represented as a rotational spring. The additional flexibility caused by crack in its vicinity is evaluated according to linear and elastic fracture mechanics theory. The WFE stiffness matrix of the crack is constructed and the algorithm for crack identification through the use of vibration-based inspection (VBI) is developed. With the accurate natural frequencies obtained from the transient signal measured, graphs of crack equivalent stiffness versus crack location are plotted, by providing the first three natural frequencies as an input. The intersection of the three curves gives the crack location and size. Experimental studies of cracked shafts are presented to demonstrate the accuracy of the method. The error in identification of crack location and size are both less than 2%. This study provides the new method for the diagnosis of incipient small crack.