A Frequency-Based Criterion for Automatic Selection of the Optimal Intrinsic Mode Function in Diagnostics of Localized Gear Tooth Faults

Abstract

To date gearboxes remain one of the most important elements of virtually every power transmission system as far as a continuous operation of the shaft line is concerned. Any failure or breakdown may result in putting the whole production line, supply chain or even peoples life in jeopardy. Endeavours to detect an incipient fault within the system serve multiple purposes from increasing the safety of the people responsible for operating the machines, through to decreasing running and operational costs, allowing time to plan for the inevitable repairs and making sure that the downtime of the machine is kept to an absolute minimum. This, in turn, makes this branch of condition monitoring of rotating machinery one of the most intensively studied. The Empirical Mode Decomposition (EMD) is a relatively new method of signal decomposition, which breaks the original signal up into a number of so-called Intrinsic Mode Functions (IMFs). The decomposition represents a type of adaptive filtering which outputs a number of IMFs which, acquired according to two strict criteria, contain portions of the filtered version of the original signal and so can carry different information about the content of the signal. EMD has already been used in the field of condition monitoring of rotating machinery, but the selection of the optimal IMF for the task often requires the experience of a condition monitoring specialist. This paper proposes a frequency-based tool for automatic selection of the IMF that is best suited for the detection of localized gear tooth faults.