A new rotating machinery fault diagnosis method based on improved local mean decomposition

Abstract

A demodulation technique based on improved local mean decomposition (LMD) is investigated in this paper. LMD heavily depends on the local mean and envelope estimate functions in the sifting process. It is well known that the moving average (MA) approach exists in many problems (such as step size selection, inaccurate results and time-consuming). Aiming at the drawbacks of MA in the smoothing process, this paper proposes a new self-adaptive analysis algorithm called optimized LMD (OLMD). In OLMD method, an alternative approach called rational Hermite interpolation is proposed to calculate local mean and envelope estimate functions using the upper and lower envelopes of a signal. Meanwhile, a reasonable bandwidth criterion is introduced to select the optimum product function (OPF) from pre-OPFs derived from rational Hermite interpolation with different shape controlling parameters in each rank. Subsequently, the orthogonality criterion (OC) is taken as the product function (PF) iterative stopping condition. The effectiveness of OLMD method is validated by the numerical simulations and applications to gearbox and roller bearing fault diagnosis. Results demonstrate that OLMD method has better fault identification capacity, which is effective in rotating machinery fault diagnosis.