Abstract
This study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.
Highlights
Machinery failures can cause severe interruption to work schedules, increasing outcomes, lower product quality and risk of health to machine operators
The fault detection methods used in this study have some features that make it possible to notice the abnormality of the worm gearbox
The distinctive features from all methods are visualized with parallel coordinates plot to get most dominant features for fault detection on worm gearbox
Summary
Machinery failures can cause severe interruption to work schedules, increasing outcomes, lower product quality and risk of health to machine operators. The vibration signals, sound signals and infrared thermography of worm gear were examined for normal and faulty conditions under different speeds. It has been observed from these studies that traditional methods and multidisciplinary methods must be applied for efficient and reliable fault detection on worm gearbox. It must be noted that these feature extraction methods are applied in the developed portable real-time condition monitoring (CM) instrument unlike the other studies presented in the literature.
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