Fault diagnosis method for rolling bearing based on deep learning and SVM (Support Vector Machine)

Abstract

The invention provides a fault diagnosis method for a rolling bearing based on a deep learning and SVM (Support Vector Machine). The method comprises using a manure learning algorithm in a deep belief network theory to complete a characteristic extraction task needed by fault diagnosis; automatically extracting the substantive characteristics of data input independent of manual selection from simple to complicate, from low to high, and automatically digging abundant information concealed in known data; in addition, classifying and identifying a test sample by adopting an SVM classification method, seeking and finding a global minimum of a target function through an effective method previously designed, so as to solve the problem that a deep belief network may be trapped into a locally optimal solution. According to the fault diagnosis method for the rolling bearing based on the deep learning and SVM provided by the invention, the accuracy and effectiveness of the fault diagnosis method for a rolling bearing can be improved, and a new effective way can be provided to solve the accuracy and effectiveness of the fault diagnosis method, therefore the fault diagnosis method can be extensively applied complex systems in chemistry, metallurgy, electric power, aviation fields and the like.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis and computer artificial intelligence, and in particular relates to a rolling bearing fault diagnosis method based on deep learning and support vector machine. Background technique [0002] Rolling bearings are one of the most important mechanical parts in rotating machinery. They are widely used in chemical industry, metallurgy, electric power, aviation and other important departments. At the same time, they are also one of the most vulnerable components. The performance and working conditions of bearings directly affect the performance of the shafts associated with them, the gears installed on the shafts, and even the entire machine equipment. Their defects will cause abnormal vibration and noise of the equipment, and even cause equipment damage. Therefore, it is particularly important to diagnose rolling bearing faults, especially for the analysis of early faults, and to avoid...

AI Technical Summary

Problems solved by technology

In the fault diagnosis method based on artificial intelligence, at present, artificial neural network is mainly used to complete the classification of the diagnosis target through continuous learning and information feedback to the system; When incomplete (missing data), the neural network cannot perform effective reasoning work, and cannot use the early characteristics of the fault to diagnose the bearing accordingly

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