Abstract
Gear failure is one of the most common causes of breakdown in rotating machineries. It is well known that vibration signals from machineries can be effectively used to detect certain gear faults. Yet it is still not an easy task to find a symptom that reflects a particular fault from vibration signals. This paper presents an advanced time-frequency signal processing technique for extracting important gear fault information from the vibration signal that is heavily corrupted by measurement noise. Experiments were performed on a bevel gearbox test rig using vibration measurements. The Time Synchronous Average (TSA) was initially utilized to eliminate all asynchronous component of vibration signal obtained from the gear. The Continuous Wavelet Transform (CWT) method was then used to capture the non-stationary behaviour of the impulse signal generated from the broken bevel gear tooth. It was shown that the diagnosis method using the Continuous Wavelet Transform combined with Time Synchronous Averaging outperformed the conventional spectral analysis, capable of identifying the angular location of broken teeth in the gear.
Highlights
Diagnostic methods using vibration from geared systems have provided a significant contribution in preventing major failures in rotating machineries
This study has demonstrated the usefulness of timefrequency signal processing of vibration signal for diagnosing the gearbox health based on experimental results from a bevel gearbox test rig
It has been shown that the Time Synchronous Average (TSA) method had the ability to identify the angular location of the gear fault, which was not possible to obtain by using the raw vibration signal alone
Summary
Diagnostic methods using vibration from geared systems have provided a significant contribution in preventing major failures in rotating machineries. The complexity of vibration signal, comprising both transient and stationary components, can lead to insufficiency of these diagnostic methods to accurately detect gearbox faults from vibration signal. The modulation sidebands that are present around the fundamental frequency or harmonics of the tooth meshing frequency can provide useful information of gear defect locations [1]. FFT assumes the underlying signal is a stationary signal, whereas the impulse generated by the gear fault is typically a transient signal. In this case, the use of timefrequency analysis has been proved to be able to detect the transient phenomena [3, 4], e.g. the impulse response generated from faulty gear. There is the need of using more sophisticated signal processing methods to provide such useful diagnostics information
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