Abstract
In recent years, early fault detection and diagnosis of gears have become extremely important due to requirement to decrease the downtime on production machinery caused by the failures. For that reason, researches have been done for the early detection of faults through the analysis of their vibration signals. Modern day machines, due to their complexities, can have many vibration generating sources in addition to noises. Therefore it is important that the vibration signal of faulty gear to be recognized and recovered for the diagnostics. In this paper Back-Propagation neural network has been used for the classification of RPM and oil level related gearbox faults that can occur during operation. With the help of Power spectrum technique, signal was more refined in order to make the feature selection process much more accurate. DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13334
Highlights
Rotating machinery, which is one of the most basic and important equipment, plays a very important role in any kind of industry [1], [2]
The vibration and sound data of gears are analysed at 4 different speeds (1200 rpm, 1500 rpm, 1800 rpm and 2100 rpm) and 3 different oil levels (130 ml, 250 ml and 850 ml)
An approach for the diagnosis of different type of faults in gear by using Mean, Fast Fourier Transform, Power spectrum and Neural Network has been presented in this paper
Summary
Rotating machinery, which is one of the most basic and important equipment, plays a very important role in any kind of industry [1], [2]. The majority of these machines are operated by the means of gears, and bearings which can become faulty with their usage and can affect the performance of the machine and can even result in their breakdown [3]. Gearboxes are considered to be one of the earliest machine parts It has been in use for thousands of years. In some types of machinery such as helicopter or aircraft, the role of gearbox is extremely important as its failure may lead to the loss of assets and even human life [4]
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