Abstract
Early identification of faults in railway gearboxes is a challenging task in gearbox fault detection. There are extensive studies, such as patents and papers have been fully developed for processing vibration signals to obtain diagnostic information about gearbox. We have proposed a new technique for detecting faults in the railway gearbox by applying the time frequency parameters and genetic algorithm neural network to deal with railway gearbox fault signals. In this method, wavelet analysis and empirical mode decomposition (EMD) are carried out on gearbox vibration signals for extracting the time-frequency feature parameters. Then genetic algorithm neural network (GNN) is used for the classifications of the time-frequency feature parameters. The analysis results show that the effectiveness and the high recognition rate in classifying different faults of railway gearboxes.
Highlights
Gearbox is the most widely used mechanical components in railway sector
The steps of time-frequency domain feature extraction are as follows: (1) The vibration signals are divided into few intrinsic mode functions (IMFs) by using the Empirical mode decomposition (EMD) method, the first n IMFs ci (t), i = 1,2, 3,n, where the most dominant fault energy is selected for extracting the feature
A method based on time-frequency feature parameters and genetic algorithm neural network (GNN) has been
Summary
Gearbox is the most widely used mechanical components in railway sector. A sudden fault in the gearbox during operation may result in heavy financial loss, it is critical to regularly investigate and check railway gear box for avoiding such kind of problems. One of the widely known technique for diagnosing gearbox faults is the vibration-based analysis [1]. Numerous condition monitoring and diagnostics methodologies are utilized for identifying the gearbox faults [2,3,4,5,6,7] These methods only provide limited effectiveness for diagnosing complicated defects. Empirical mode decomposition (EMD) method is based on the local characteristic time scale of signal. It can split the complicated signal into a number of intrinsic mode functions (IMFs). 1874-155X/14 railway gearbox fault detection by using time-frequency feature parameters and genetic algorithm neural network. Where xi is ith sampling point of the signal x ; n is the number of points in the signal, xrms is the root mean square of the signal, xr is the square root of amplitude of the signal, and x is the absolute average of the signal
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.
