Bearing fault diagnosis method based on symbolic probabilistic finite state machine

A finite state machine and fault diagnosis technology, applied in the direction of mechanical bearing testing, etc., can solve the problems of KNN classification algorithm efficiency reduction, time increase, learning to obtain classifiers, etc., to improve computing efficiency and fault classification effect, improve stability and performance. The effect of practicality

Active Publication Date: 2015-09-23
SOUTHEAST UNIV
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Problems solved by technology

However, the KNN algorithm is a lazy learning algorithm based on instance learning. It does not learn an explicit classifier from the training set, which makes it necessary to save all training samples.
With the increase of the train

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  • Bearing fault diagnosis method based on symbolic probabilistic finite state machine
  • Bearing fault diagnosis method based on symbolic probabilistic finite state machine

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[0024] The technical solutions of the invention will be described in detail below in conjunction with the accompanying drawings and examples, but the protection scope of the present invention is not limited to the examples.

[0025] Such as figure 1 As shown, the present invention provides a bearing fault diagnosis method based on a symbolic probability finite state machine, the method comprising the following steps:

[0026] (1) Use the acceleration sensor to collect a large number of bearing signals of known fault categories, including 30 groups of normal bearing vibration signals, 30 groups of 0.18mm inner ring faults, 30 groups of 0.36mm inner ring faults and 30 0.54mm inner ring faults Group. The bearing vibration signals of different fault categories are marked according to the category, and there are 4 different types of bearing signals, which are respectively marked as c 1 , c 2 , c 3 , c 4 , the number of samples for each type of bearing signal is 30, and there a...

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Abstract

The invention discloses a bearing fault diagnosis method based on a symbolic probabilistic finite state machine. The method comprises the steps that a one-dimensional time series with concentrated training data is converted to a two-dimensional symbol matrix; a probabilistic finite state machine model is constructed for the two-dimensional symbol matrix; an extracted left feature vector is used as a feature value to represent an original bearing signal; and finally an improved K- nearest neighbor classification (KNN) algorithm is used to learn the left feature vector of a bearing signal of a known failure category. For a bearing signal to be diagnosed, the same feature extraction method is used, and then the improved KNN algorithm is used to realize fault diagnosis. According to the invention, a K-means clustering method is used to improve the traditional K- nearest neighbor classification algorithm, and the computation efficiency and the fault diagnosis effect of the bearing diagnosis algorithm are improved.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method based on symbolic dynamics. Background technique [0002] With the rapid development of science and technology and modern industry, machinery and equipment in the machinery, energy, petrochemical, transportation and national defense industries of the national economy are increasingly large, high-speed, integrated and automated, which provides a strong guarantee for the rapid development of my country's economy . However, catastrophic accidents caused by mechanical equipment failures occur frequently. If the abnormal state of the mechanical system can be accurately and timely identified, it will be of great significance to the safe operation of the mechanical system and avoid major and catastrophic accidents. With the demand for high quality, low energy consumption and safe production, a variety of signal processing methods have bee...

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Application Information

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IPC IPC(8): G01M13/04
Inventor 严如强胡世杰
Owner SOUTHEAST UNIV
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