Previous studies have indicated that the pipe-surface-mounted helical strakes effectively reduce vortex-induced vibration (VIV) under a uniform flow application, particularly during the lock-in region. Since VIV experiments are time-consuming, observation is generated with an interval helical strakes parameter in pitch and height to lessen tedious procedures and repetitive post-processing analyses. The aforementioned result subset is insufficient for helical strakes design optimisation because the trade-off between the helical strakes dimension, lock-in region and flow velocity are non-trivial. Thus, a parametric model based on an improved recursive least squares (RLS) parameter estimation technique is proposed to define the statistical relationship between input, or strakes and pipe dimension, and output, or VIV amplitude ratio. As results suggested, revised RLS estimated VIV model demonstrated an optimal prediction with the highest coefficient of determination and lowest Integral Absolute Error. The feasibility of VIV parametric model was validated by embed into Genetic Algorithm (GA) as the fitness function to acquire a desirable helical strakes dimension with minimum VIV amplitude. The rapid generation of optimal helical strakes dimension which returned the highest VIV suppression implied a superior simulation method compared to the experimental outcome.
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.