This paper presents and investigates recursive order tracking (OT) techniques based on the least mean-square (lms) method and the Vold-Kalman (VK) algorithm with a one pole structural equation, both of which could be ...
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This paper presents and investigates recursive order tracking (OT) techniques based on the least mean-square (lms) method and the Vold-Kalman (VK) algorithm with a one pole structural equation, both of which could be realized as real-time applications. Additionally, for comparisons, two common adaptive OT filters are considered: the recursive least-squares (RLS) method and the VK algorithm with a two pole structural equation. The numerical implementations of the considered methods, through simulations on a representative noisy synthetic signal, including both close and crossing orders spectral components, are performed. The results indicate a possible degradation in the tracking performance of the RLS algorithm and the effectiveness of the simple lms method, as well as both considered VK algorithms, for OT and distinguishing. The influence of the sampling frequency on the choosing of a weighting factor for the VK recursive OT filters is further investigated to extend the guidelines from the literature for using these methods. Two examples of practical implementations of the considered recursive OT methods are given: (i) the separation of the crossing orders arising in the laboratory test bench of the two unbalanced rotors rotating at independent frequencies and (ii) the one engineering application of fault diagnosis in the massive reduction gear on an excavator with a cable crowd, based on the distinguishing and separation of close orders. The experimental results justify the implementation of the proposed techniques as OT tools in industrial applications.
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