Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox

Gears are common power transmission elements and are frequently responsible for transmission failures. Instantaneous time-frequency spectrum (ITFS) resulted from local mean decomposition is applied to the surveillance and early fault diagnosis of a finishing rolling mill in this paper. Results of practical signals demonstrate that ITFS is effective and reliable for the early detection of gear local fault. In addition, a new parameter to evaluate the damage severity of the gearbox is also developed based on the marginal spectrum derived from ITFS. The utility of the new gear fault symptom has been investigated using practical vibration signals. Results show that the new parameter is only sensitive to the changes caused by the deterioration of a monitored unit and insensitive to the influence of the variable non-deterioration factors such as varying speed and loads. This new index may thus find its wide applications for machine prognostics in the near future.