Affiliation of Author(s):航空学院
Journal:JOURNAL OF CENTRAL SOUTH UNIVERSITY
Key Words:intelligent fault diagnosis short time Fourier transform sparse filtering softmax regression
Abstract:Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis. However, the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation. To overcome this deficiency, a novel intelligent defect detection framework based on time-frequency transformation is presented in this work. In the framework, the samples under one speed are employed for training sparse filtering model, and the remaining samples under different speeds are adopted for testing the effectiveness. Our proposed approach contains two stages: 1) the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm, and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm; 2) different defect types are classified by the softmax regression using the defect features. The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment. The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds, but also obtains higher identification accuracy than the other methods.
ISSN No.:2095-2899
Translation or Not:no
Date of Publication:2019-06-01
Co-author:Zhang Zhong-wei,lsz,Wang Jin-rui
Correspondence Author:CHEN Huaihai
Date of Publication:2019-06-01