Assessment of local faults in helical geared system using vibro-acoustic signals based on higher order spectrum analysis. (15th March 2023) - Gunasegaran, V.; Amarnath, M.; Chelladurai, H.; Krishna, I.R.Praveen
Highlights: Vibration and acoustic signals are formulated to detect and diagnose faults developed in the geared system. Higher order spectrum analysis improves the QPC for identifying system nonlinearity. Promising gear fault related features can extract from bicoherence in the form of coupled frequencies. Monitoring and analysis of statistical performance parameters of the stochastic system used in the overall study. Abstract: Geared transmission system plays an important role in modern technology to transmit both power and motion. The power transmission and control requirements in manufacturing, aviation, transportation, energy industries require uninterrupted operation under high efficiency. Increase in fatigue load cycles in a geared transmission system thereby leading to negative impact on contact surfaces of gear tooth, which result in non-unifor m power transmission, increase in dynamic response, and decrease in efficiency followed by catastrophic tooth failure. The wear propagated on gear teeth contact causes a variation in the dynamic response of geared system. It is widely accepted that the local/distributed faults developed on rolling/sliding contact elements can be detected by using vibration and/or acoustic monitoring and analysis techniques. This paper describes the results of experimental studies carried out to detect and diagnose the simulated faults in the helical gear tooth of a two stage helical gearbox. The helical gear was mounted in a two-stage gearbox operating under controlled speed and load conditions. The vibration and airborne sound signals obtained from the gearbox were considered to attain a reliable fault related information by using a higher-order spectrum analysis technique. The fault related features extracted from the vibration and the acoustic data post processed by using bispectrum analysis provided a better diagnostic information to detect progressive faulty operating conditions viz., 20%, 40%, 60%, 80% and 100% tooth removal cases in the gearbox.