Shallow machine learning fault diagnosis for industrial equipment based on vibration data

In order to improve the maintenance efficiency of industrial equipment and ensure its safe and stable operation, it is of great significance to carry out effective fault diagnosis of industrial equipment. The fault diagnosis by analyzing the vibration signal of industrial equipment has great feasibility and advantages. This paper firstly elucidates the significance of fault diagnosis based on vibration signals. Subsequently, it expounds on the current research status of shallow machine learning fault diagnosis in terms of signal noise reduction, feature extraction, and feature dimensionality reduction. The advantages and limitations of typical methods used in this technology are summarized, along with the identified issues that need to be addressed and future developmental directions.