Abstract
As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets.
Highlights
Rolling bearings are vital components of mechanical systems and have been widely used in rotating machinery
A rolling bearing fault feature extraction method based on the IMOMEDATKEO is proposed
Some conclusions are obtained as follows: (1) The envelope harmonic-to-noise ratio (EHNR) method is introduced to obtain the EHNR spectrum of the original signal and solves the problem that the fault period T of Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) depends on prior knowledge
Summary
Rolling bearings are vital components of mechanical systems and have been widely used in rotating machinery. The fault diagnosis technique based on vibration signal has been widely used in practice because of its intuitive operation and reliable diagnosis results, where the vibration signal of the bearing is monitored by the acceleration sensors installed in the appropriate direction of the bearing seat or the box, and the signal is analyzed and processed to determine the bearing condition. Gao et al [9] designed a wireless sensor vibrating signal acquisition and processing system, and the researchers could collect and analyze the signal timely and conveniently. Verstraete et al [11] presented multi-sensor fusion systems for integrated remaining useful life prognostic capabilities
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