Abstract
Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA.
Highlights
Machine learning is the most efficient tool in eliminating human intervention or assisting human supervision in machinery fault diagnosis
Further analysis showed that the proposed feature selection tree (FST) was able to select an equivalent or better feature subset than genetic algorithm (GA) to represent the entire dataset
The proposed FST selected identical features as the GA for bearing data, the execution time for the proposed FST was reduced by up to 92% compared to the GA
Summary
Machine learning is the most efficient tool in eliminating human intervention or assisting human supervision in machinery fault diagnosis. The wrapper method remains an attractive feature selection option as it selects the best combination of features based on its performance with the desired classifier, a trade-off between computational cost and prediction accuracy is non-trivial. The following case studies explicitly show that GAs opting for large, bulky matrices [17] reduced the partial least square (PLS) model complexity by means of adequately tuned GA feature selection and feature elimination, resulting in better near-infrared (NIR) spectral resolution and interpretation, leading to end result enhancement By comparing this to the grid algorithm, Huang and Wang obtained better SVM classification performance by simultaneously fine-tuning the feature subset option and the kernel’s parameter using GA [18]. A trade-off relationship between accuracy and computational effort (implementation of the algorithm, parameter setting, calculation time and outcome interpretation) is inevitable for the GA wrapper method non-spatial dataset measurement
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