Bearing fault diagnosis method based on fuzzy support vector machine

Abstract

The invention discloses a bearing fault diagnosis method based on a fuzzy support vector machine. Three faults, namely an inner ring single-point fault, an outer ring single-point fault and a ball single-point fault, are taken for example, and fault feature extraction, including demodulation on a time domain feature parameter and a vibration signal by Hilber transformation, is carried out by combining normal operation of a bearing; a demodulated signal is subjected to spectral analysis so as to find a frequency domain fault feature frequency. These feature parameters form a training sample and a test sample; a fuzzy membership degree is added into the training sample by a fuzzy C means clustering algorithm, and the fault judgment is carried out by a support vector machine multi-classification method. A fault diagnosis example analysis part displays the bearing fault diagnosis correctness of a constructed FSVM (fuzzy support vector machine) model, so that high classification performance and high noise resistance are achieved, and a theoretical method is provided in order to avoid accidents, economical loss and the like which are caused by the bearing fault; the bearing fault diagnosis method has important reference value.

Description

technical field [0001] The invention belongs to the field of fault diagnosis, and relates to a fault diagnosis method based on the combination of fuzzy C-means clustering and support vector machine established for bearing faults. Background technique [0002] Rolling bearings are the most widely used mechanical parts in electric power, metallurgy, petrochemical, machinery, aerospace and some military industrial sectors, and are also one of the most vulnerable parts of mechanical equipment. Relevant statistics show that: in rotating machinery using bearings, about 30% of mechanical failures are caused by bearings. This is because the bearing is the part with the worst working conditions in the mechanical equipment. It plays the role of bearing and transmitting the load in the mechanical equipment. The working conditions are complicated. Whether its operating status is normal often directly affects the performance of the entire machine. Faults in the working state will cause ...

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Problems solved by technology

However, some scholars do not consider the interference of abnormal points in the bearing fault data, and some use neural networks or FCM for classification, and the fault diagnosis of bearings is a small sample problem

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