An improved bearing fault detection strategy based on artificial bee colony algorithm

TL;DR

This paper introduces an enhanced fault detection method for bearings using shapelet transformation, an improved artificial bee colony algorithm, and XGBoost, achieving high accuracy in identifying faults from vibration signals.

Contribution

It proposes a novel fault detection framework combining shapelet transformation and an improved ABC algorithm to optimize XGBoost parameters, improving accuracy over existing methods.

Findings

Fault recognition accuracy reaches 97% with the proposed method.

Improved ABC algorithm avoids local optima in parameter optimization.

Classification accuracy increases from 97.02% to 98.60% with optimization.

Abstract

The operating state of bearing directly affects the performance of rotating machinery and how to accurately and decisively extract features from the original vibration signal and recognize the faulty parts as early as possible is very critical. In this study, the one-dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelets transformation is proposed to calculate the parameter of it which is also the standard deviation of the transformed shaplets that is usually selected by trial and error. Moreover, XGBoost is used to recognize the faults from the obtained features, and an improved artificial bee colony algorithm(ABC) where the evolution is guided by the importance indices of different search space is proposed to optimize the parameters of XGBoost. Here the value of importance index is related to the probability of optimal solutions in certain space, thus the problem of easily falling into local optimality in traditional ABC could be avoided.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97% which is much higher than the ones corresponding to other extraction strategies, thus the ability of extraction could be improved. And with the improved artificial bee colony algorithm which is used to optimize the parameters of XGBoost, the classification accuracy could be improved from 97.02% to about 98.60% compared with the traditional classification strategy