Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation,\r\ngearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting\r\ngearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A\r\nmodified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two\r\naccelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured\r\nshaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with\r\na numerous values of step size. Findings confirm that 10-5 step size invariably produces more accurate results and there has\r\nbeen a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more\r\ndiscernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a\r\nhealthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth\r\nremoval) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear\r\ncondition features. This paper illustrates that the new approach offers a more effective way to detect early faults.
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