An Improved Viterbi Algorithm for Adaptive Instantaneous Angular Speed Estimation and Its Application Into the Machine Fault Diagnosis

Abstract

It is a very challenging task to diagnose the fault of rotating machinery under variable speed condition. The traditional tacholess order tracking (TLOT) method still has the problems of high computation complexity and low adaptability. Aiming at these problems, a new adaptive instantaneous angular speed (IAS) estimation method based on the improved Viterbi algorithm (VA) and the energy centrobaric correction method is developed. Since the traditional VA cannot process the long time series and find the fundamental order automatically, an improved penalty function for ridge search is proposed. Meanwhile, the search band of IAS is adaptively optimized to accelerate the speed of ridge search. Furthermore, an energy centrobaric correction approach is used for improving the accuracy of instantaneous frequency estimation and the antinoise performance. With the obtained IAS, the nonstationary time-domain vibration signal is resampled as the stationary angle-domain vibration signal by the Vold–Kalman filtering and the Hilbert transform. Finally, the envelope order spectrum is used to diagnose the mechanical faults of a civil aircraft engine. With the datasets of aircraft engine, rolling bearing, and wind turbine, the experimental results show that the improved VA can adaptively estimate IAS faster and achieve higher accuracy of fault detection than the typical IAS estimation methods. Meanwhile, its antinoise ability is verified.