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With the development of machine learning, a data-driven model has been widelyused in vibration signal fault diagnosis. Most data-driven machine learningalgorithms are built based on well-designed features, but feature extraction isusually required to be completed in advance. In the deep learning era, featureextraction and classifier learning are conducted simultaneously, which willlead to an end-to-end learning system. This paper explores which one of the twokey factors, i.e., feature extraction and classification algorithm, is moreessential for a specific task of vibration signal diagnosis during a learningsystem is generated. Feature extractions from vibration signal based on bothwell-known Gaussian model and statistical characteristics are discussed,respectively. And several classification algorithms are selected toexperimentally validate the comparative impact of both feature extraction andclassification algorithm on prediction performance.

Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis