Abstract
For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.
Highlights
Rolled bearings are widely applied in rotating machinery, which running status will directly influent the function of the whole machine
Using support vector machine (SVM) parameters without optimization, the experimental results showed that the total correct classification rate is 90.7%
The correct classification of faults can be given by the SVM model; the experimental results showed that the total correct recognition rate is 83.70%, which is smaller than the correct recognition rates 93.33% and 99.5% from the proposed methods in this paper
Summary
Rolled bearings are widely applied in rotating machinery, which running status will directly influent the function of the whole machine. The intervals of mean value and variance are brought into an expert estimation p-box modeling method to obtain DSS. Method 1, Obtain cumulative width to use basic probability distribution for all focal element interval of weight, which could be expressed as [4]
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