Gear wear process monitoring using acoustic signals

Airborne acoustic signals contain valuable information from machines and can be detected remotely for condition monitoring. However, the signal is often seriously contaminated by various noises from the environment as well as nearby machines. This paper presents an acoustic based method of monitoring a two stage helical gearbox, a common power transmission system used in various industries. A single microphone is employed to measure the acoustics of the gearbox under-going a run-to-failure test. To suppress the background noise and interferences from nearby ma-chines a modulation signal bispectrum (MSB) analysis is applied to the signal. It is shown that the analysis allows the meshing frequency components and the associated shaft modulating components to be captured more accurately to set up a clear monitoring trend to indicate the tooth wear of the gears under test. The results demonstrate that acoustic signals in conjunction with efficient signal processing methods provide an effective monitoring of the gear transmission process

Condition Monitoring and Fault Diagnosis of a Multi-Stage Gear Transmission Using Vibro-acoustic Signals

Gearbox condition monitoring(CM) plays a vital role in ensuring the reliability and operational efficiency of a wide range of industrial facilities such as wind turbines and helicopters. Many technologies have been investigated intensively for more accurate CM of rotating machines with using vibro-acoustic signature analysis. However, a comparison of CM performances between surface vibrations and airborne acoustics has not been carried out with the use of emerging signal processing techniques. This research has focused on a symmetric evaluation of CM performances using vibrations obtained from the surface of a multi stage gearbox housing and the airborne sound obtained remotely but close to the gearbox, in conjunction with state of the art signal processing techniques, in order to provide efficient and effective CM for gear transmissions subject to gradual and progressive deteriorations. By completing the comparative studies, this research has resulted in a number of new findings that show significant contributions to knowledge which are detailed as follows. In general, through a comprehensive review of the advancement in the subject, the research has been carried out by integrating an improved dynamic modelling, more realistic experiment verification and more advanced signal processing approaches. The improved modelling has led to an in-depth understanding of the nonlinear modulation in vibro-acoustic signals due to wear effects. Thereafter, Time Synchronous Average (TSA) and Modulation Signal Bispectrum (MSB) are identified to be the most promising signal processing methods to fulfil the evaluation because of their unique properties of simultaneous noise reduction and modulation enhancement. The more realistic tests have demonstrated that arun-to-failure test is necessary to develop effective diagnostic tools as it produces datasets from gear transmissions where deterioration naturally progresses over a long operation, rather than faults created artificially to gear systems, as is common in the majority of studies and the results unreliable. Particularly, the evaluation studies have clarified a number of key issues in the realisation of gearbox diagnostics based on TSA and MSB analysis of the vibrations from two accelerometers and acoustics from two microphones in monitoring the run-to-failure process, which showed slight gear wear of two back-to-back multiple stage helical gearboxes under variable load and speed operations. TSA analysis of vibration signals and acoustic signals allows for accurate monitoring and diagnosis results of the gradual deterioration in the lower speed transmission of both the tested gearboxes. However, it cannot give the correct indication of the higher speed stages in the second gearbox as the reference angle signal is too erroneous due to the distortion of long transmission trains. In addition, acoustic signals can indicate that there is a small determination in the higher speed transmission of the first gearbox. The MSB analysis of vibration signals and sound signals allows for the gathering of more corrective monitoring and diagnostic results of the deterioration in the four stages of transmissions of the two tested gearboxes. MSB magnitudes of both the two lower speed transmissions show monotonic increases with operational time and the increments over a longer period are in excess of three times higher than the baselines, the deteriorations are therefore regarded as severe. For the two higher speed transmissions, the MSB of vibrations and acoustics illustrates small deteriorations in the latter operating hours. Comparatively, acoustic signal based diagnostics can out-perform vibration as it can provide an early indication of deteriorations and correct diagnosis of the faults as microphones perceive a large area of dynamic responses from gearbox housing whereas accelerometers collect a very localised response which can be distorted by transmission paths. In addition, MSB analysis can out-perform conventional TSA as it maintains all diagnostic information regarding the rotating systems and can be implemented without any additional reference channels

Fatigue Prediction of a Gear Transmission System based on Vibro-Acoustic Measurements

Gearing components play a pivotal role in most power transmission mechanisms but their application in industry consistently results in significant disruption and losses due to gear failures. This emphasises the significance of condition monitoring and fault diagnostics techniques of gear transmission systems to enhance overall safety and operational reliability in order to minimise gearbox failure rates and their associated disruption and losses to industry. In a wide range of industrial contexts, vibration analysis is extensively used for machinery condition monitoring and diagnostics due to its good detection results. As vibration and acoustic noise have the same generation mechanism, acoustic noise can also be used for machinery condition monitoring combined with effective signal processing methods. In this paper, vibration and acoustic signals were both used to analyse the fatigue process of a gear transmission system based on synchronous vibro-acoustic measurements. In order to enhance the signal-to-noise ratio (SNR) of the measured signals, time synchronous average (TSA) technique was employed to pre-process the vibration and acoustic signals. The side band energy ratio (SER) is then extracted to predict the vibration and acoustic signals to indicate the fatigue process of the testing gearbox under different operating conditions. Further the fatigue process was detected through analysing the measured signals in the high frequency band as these are less contaminated by background reverberation interferences. The key results show that the gear transmission system can be monitored by vibration and acoustic analysis which show similar trend results for the gear fatigue process tested

Monitoring and diagnosing the natural deterioration of multi-stage helical gearboxes based on modulation signal bispectrum analysis of vibrations

This paper presents a novel method for diagnosing the gradual deterioration of gears using modulation signal bispectrum (MSB) and vibration measurements. A nonlinear model was derived to understand dynamic forces applied to gears that are excited by quadratic terms, e.g., shaft rotating speeds and gear meshing frequencies. Owing to its sensitivity to those quadratic terms, MSB is powerful in recovering less noisy condition related features from the measured vibration signals, e.g., gear meshing and multiples of shaft rotating speed. This allows a more pronounced representation of gear dynamic forces and makes it more effective for detecting early gear deterioration. The proposed method was verified through a run-to-failure test based on a helical gearbox system. The results show small gears at low-speed stages deteriorate faster and fail at 838 hours. This was because they prone to wear more severe due to poorer lubrication conditions compared with gears at high-speed stages. Moreover, fault detectability of the developed MSB-based method outperforms that of time synchronous averaging (TSA). Compared to TSA, clearer signs of early gear deterioration were captured using MSB, which makes it a more powerful tool for monitoring the condition of gearboxes