A novel technique has been developed in [1] to remove the effect of the transfer function from gear transmission error (TE) signals, using cepstrum-based operational modal analysis (OMA). The transfer function of TE can be identified by applying the cepstrum-based OMA method to the random part of the TE signal. This technique is capable of reconstructing equivalent GTE or STE from a single DTE measurement at a moderate/high speed. With signals measured under two or more loads, it allows the separation and identification of the geometric profile error and stiffness profile, with high resolution.
[1] R. Lu, M.R. Shahriar, P. Borghesani, R.B. Randall, Z. Peng, Removal of transfer function effects from transmission error measurements using cepstrum-based operational modal analysis, Mech. Syst. Signal Process. 165 (2022) 108324. https://doi.org/10.1016/J.YMSSP.2021.108324.
A series of new approaches for tracking the natural evolution of bearing spall size have been developed in this project. Firstly, an acceleration-based method was proposed to use the duration of spall induced natural frequency perturbation to estimate the spall size [2]. Then a benchmark study was conducted to compare the performance of different measurement approaches (acceleration, acoustic emission, angular speed, radial displacement and radial load) in estimating bearing fault severity [3]. The best performance was found on the radial load and radial displacement signals, and a reliable severity estimation method was proposed accordingly (to be published).
[2] H. Zhang, P. Borghesani, W. A. Smith, R. B. Randall, M. R. Shahriar, and Z. Peng, “Tracking the natural evolution of bearing spall size using cyclic natural frequency perturbations in vibration signals,” Mech. Syst. Signal Process., vol. 151, p. 107376, Apr. 2021, doi: 10.1016/j.ymssp.2020.107376.
[3] H. Zhang, P. Borghesani, R. B. Randall, and Z. Peng, “A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings,” Mech. Syst. Signal Process., vol. 166, p. 108466, Mar. 2022, doi: 10.1016/j.ymssp.2021.108466.
A recent development has demonstrated the practical use of transmission error (TE) in the severity assessment of gear cracks [4]. The technique employs a multiple-speed and multiple-load approach to estimate the profile error and meshing compliance of the gears. This is done by first removing transfer-path effects from measured TE signals, and then separating the components of geometric imperfections and deflections. The meshing compliance, which consists of symptoms of the crack, is then used to accurately estimate crack severity, via a series of automated steps including crack detection and comparison with a theoretical mesh-stiffness model.
[4] Zhan Yie Chin, Pietro Borghesani, Yuanning Mao, Wade A. Smith, Robert B. Randall, Use of transmission error for a quantitative estimation of root-crack severity in gears, Mech. Syst. Signal Process. 171 (2022) 108957. https://doi.org/10.1016/j.ymssp.2022.108957.
Ever wonder if it is possible to measure wear depth on gears directly in microns using signal-based techniques, while the gear is in operation? The UNSW Tribology and Machine Condition Monitoring group looked into this problem and developed a novel approach to obtain “absolute transmission error” that measures both the average wear depth and profile changes on gears [5]. The technique is simple to implement and does not require sophisticated signal processing, yet it’s robust in assessing gear wear severity.
[5] Zhan Yie Chin, Wade A. Smith, Pietro Borghesani, Robert B. Randall, and Zhongxiao Peng, “Absolute transmission error: A simple new tool for assessing gear wear,” Mech. Syst. Signal Process., vol. 146, p. 107070, Jan. 2021, doi: 10.1016/j.ymssp.2020.107070.
A recent collaborative work between the UNSW Tribology and Machine Condition Monitoring group, INSA-Lyon and our industry partner takes a fresh new look at bearing signal models [6]. Cyclostationary and pseudo-cyclostationary models were proposed over 20 years ago and have fuelled the development of increasingly advanced bearing diagnostic techniques, yet the models themselves have not been examined in much detail. This work studies the physical justification of the two models and explains the effects of key model parameters on the signal properties, providing a platform for the development of the next generation of diagnostic techniques.
[6] P. Borghesani, W.A. Smith, R.B. Randall, J. Antoni, M. El Badaoui, Z. Peng, “Bearing signal models and their effect on bearing diagnostics”, Mech. Syst. Signal Process., vol. 174, p. 109077, 2022.
