Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis
This work addresses the problem of optimizing machine learning systems for vibration signal diagnosis, but it is incremental as it compares existing methods without introducing new techniques.
This paper investigates whether feature extraction or classification algorithms are more critical for vibration signal fault diagnosis, finding that feature extraction has a greater impact on prediction performance in their experimental study.
With the development of machine learning, a data-driven model has been widely used in vibration signal fault diagnosis. Most data-driven machine learning algorithms are built based on well-designed features, but feature extraction is usually required to be completed in advance. In the deep learning era, feature extraction and classifier learning are conducted simultaneously, which will lead to an end-to-end learning system. This paper explores which one of the two key factors, i.e., feature extraction and classification algorithm, is more essential for a specific task of vibration signal diagnosis during a learning system is generated. Feature extractions from vibration signal based on both well-known Gaussian model and statistical characteristics are discussed, respectively. And several classification algorithms are selected to experimentally validate the comparative impact of both feature extraction and classification algorithm on prediction performance.