Rolling bearing fault classification method based on mixed feature extraction

Abstract

The invention discloses a rolling bearing fault classification method based on mixed feature extraction. The rolling bearing fault classification method comprises the steps: firstly, acquiring a mixedfeature set composed of waveform features, time domain features, frequency domain features and the like of signals; introducing the internal class compactness and the internal overlap into a sequenceforward selection algorithm, and extracting a suboptimal feature group in the mixed features as the input of an enhanced KNN classifier; and finally, based on distance and density calculation, obtaining an optimal average classification probability, outputting an optimal feature group, marking a fault state corresponding to the feature group, and realizing intelligent classification of rolling bearing faults. According to the invention, the interference of correlation and redundancy between fault signals on the fault classification accuracy is effectively reduced; according to the KNN classifier, the capability that a traditional KNN classifier only adopts distance calculation for classification is improved, and the problem that the traditional KNN classifier is influenced by K value sensitivity and is not beneficial to classification of an intelligent algorithm is solved, and finally the classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rolling bearings, key components in rotating mechanical devices, and in particular relates to a rolling bearing fault classification method based on mixed feature extraction. Background technique [0002] Rolling bearings are a key component of rotating machinery, and their performance directly affects the operation of the entire equipment. According to statistics, more than 40% of all mechanical failures are caused by bearing failures; about 70% of rotating machinery failures are rolling bearing failures; among gearbox failures, bearing failures account for about 19%; motors 80% of equipment failures are bearing failures. Therefore, it is of great significance to carry out fault diagnosis on rolling bearings. The traditional model-based fault diagnosis method is time-consuming and labor-intensive through signal processing technology and feature extraction. In fact, due to the complex...

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Problems solved by technology

The traditional model-based fault diagnosis method is time-consuming and labor-intensive through signal processing technology and feature extraction. In fact, due to the complexity of the system's failure modes and failure mechanisms, model establishment requires a large amount of mathematical and mechanical knowledge, coupled with various experimental verifications. At the same time, It is difficult to avoid errors and unknown interference in the modeling process, which makes it difficult to establish a diagnostic model; in addition, although an isolated single feature or a single time-frequency domain feature can be used as a basis for fault diagnosis or state evaluation at a certain point in time, it cannot Accurately describe the whole life cycle process of rolling bearing performance degradation, there is a problem of insufficient characterization ability, which seriously affects the accuracy of bearing reliability analysis and fault diagnosis

Although multi-domain features, such as time-frequency domain, time domain, and frequency domain, can fully reflect the state of the bearing life cycle, when there are too many features, not only the amount of data increases in series, but also there is cross redundancy. Therefore, how to effectively It is a major challenge for rolling bearing fault diagnosis to select features that contribute greatly to characterizing the fault characteristics and have direct correlation, and reduce the intersection between features and reduce information redundancy.

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