Abstract
In order to extract impulse components from bearing vibration signals with strong background noise, a fault feature extraction method based on multi-scale average combination difference morphological filter and Frequency-Weighted Energy Operator is proposed in this paper. The average combination difference morphological filter (ACDIF) is used to enhance the positive and negative impulse components in the signal. The double-dot structure element (SE) is used instead of zero amplitude flat SE to improve the effectiveness of fault feature extraction. The weight coefficients of the filtered results at different scales in multi-scale ACDIF are adaptively determined by an optimization algorithm called hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC). At last, as the Frequency-Weighted Energy Operator (FWEO) outperforms the enveloping method in detecting impulse components of signals, the filtered signal is processed by FWEO to extract the fault features of bearings. Results on simulation and experimental bearing vibration signals show that the proposed method can effectively suppress noise and extract the fault features from bearing vibration signals.
Highlights
Vibration signals are often used in the condition monitoring and fault diagnosis of mechanical equipment
Research shows that the Frequency-Weighted Energy Operator (FWEO) method is able to detect signal impulsiveness and improves performance over the Teager-Kaiser Energy Operator (TKEO) and enveloping method [28]
This paper has proposed a new fault feature extraction method for rolling element bearings that can achieve good result by using multi-scale average combination difference morphological filter (ACDIF) and FWEO
Summary
Vibration signals are often used in the condition monitoring and fault diagnosis of mechanical equipment. FAULT FEATURE EXTRACTION FOR ROLLING ELEMENT BEARINGS BASED ON MULTI-SCALE MORPHOLOGICAL FILTER AND FREQUENCYWEIGHTED ENERGY OPERATOR. Multi-scale morphology filter has been widely used [18, 19], but when the scale is too large, the morphology filter will ignore some useful components in the original signal when reducing noise, which may lead to the loss of fault features. Y. Li [21] et al pointed out the existing problems of multi-scale morphology, diagonal slice spectrum is introduced to select the optimal scale in morphology analysis, which is proved to be capable of extracting the impulsive feature of the bearing vibration signals. In this paper, multi-scale ACDIF and Frequency-Weighted Energy Operator (FWEO) is used to extract fault features from bearing vibration signals immersed in heavy background noise.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
