Abstract
Considering the difficulty in the diagnosis of compound faults in rolling bearings, the paper combines Intrinsic Time-scale Decomposition (ITD) and Singular Value Decomposition (SVD) for extracting the characteristics of compound faults from rolling bearings. Rotational components obtained from ITD decomposition are denoised according to Singular Value Decomposition algorithm; signal is reconstructed by denoised rotational components; at last, characteristics of compound faults of rolling bearings are extracted by Hilbert spectrum envelope of reconstructed signal. In validation, the paper has made a comparative study on the proposed ITD-SVD method and conventional one based on ITD algorithm and PCA method, and the result shows that ITD-SVD method works better on noise control and thereby provides more precise extraction of characteristic frequency of compound faults from rolling bearings of aero-engine.
Highlights
It is of great significance to make fault diagnosis of rolling bearings, one of the important and vulnerable parts in large-sized rotary machines like aero-engine [1]
To validate the effectiveness of method, the paper has made a comparative study on Intrinsic Time-scale Decomposition (ITD)-Singular Value Decomposition (SVD) and usual methods based on ITD algorithm in extracting characteristics of rolling bearings
ITD is combined with SVD to extract the characteristics of compound faults in rolling bearings and specific process is shown as follow: Step 1: vibration signal is decomposed into several proper rotation components (PR) components and residual trend components based on ITD algorithm; Step 2: each rotational component is denoised based on singular value difference spectrum algorithm; Step 3: vibration signal is reconstructed with rotational components denoised; Step 4: reconstructed signal is subjected to envelope analysis based on Hilbert envelope spectrum; characteristics of compound faults are extracted and fault identification is studied
Summary
It is of great significance to make fault diagnosis of rolling bearings, one of the important and vulnerable parts in large-sized rotary machines like aero-engine [1]. Zhang decomposed signal with ITD algorithm when studying single fault of rolling bearings, and proceeded to fault diagnosis with the PR components of larger kurtosis [19]. The paper has combined ITD algorithm and SVD method to study the characteristic extraction and fault diagnosis in the early stage of compound faults in rolling bearings. To validate the effectiveness of method, the paper has made a comparative study on ITD-SVD and usual methods based on ITD algorithm in extracting characteristics of rolling bearings (scheme A: proper rotation components obtained after ITD decomposition are reconstructed without single trend components; scheme B: ITD is combined with maximum kurtosis)
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