Intelligent fault diagnosis method based on circulation neural network

Abstract

The invention discloses an intelligent fault diagnosis method based on a circulation neural network. The method comprises following steps of (1) by use of an acceleration sensor, acquiring time sequence vibration signals of a rotary machine during work in different health states, and dividing acquired original vibration signals into a training set and a test set; (2) establishing a circulation neural network; (3) training the circulation neural network; (4) testing the trained circulation neural network, according to a classification result, judging whether the network reaches an expected diagnosis target, and if the accuracy is lower than the expected value, repeating the step (3) until the circulation neural network whose accuracy is higher than the expected value is acquired; and (5) carrying out intelligent fault diagnosis through the circulation neural network obtained in the step (4). According to the invention, by use of the modeling ability for sequence information of the circulation neural network, the original time sequence vibration signals are directly processed, so faults of the rotary machine can be precisely diagnosed by use of lesser information, and quite high identification speed is achieved.

Description

technical field [0001] The invention relates to the technical field of processing vibration signals of rotating machinery, in particular to an intelligent fault diagnosis method based on a cyclic neural network. Background technique [0002] The fault diagnosis method mainly focuses on the operating state of the system, which can detect faults in time and guide maintenance, which plays an important role in improving system reliability. In general, when a rotating component fails, it will be accompanied by a change in the form of vibration, resulting in an instantaneous vibration pulse. Therefore, the vibration signal carries important diagnostic information and is an important basis for equipment status identification. Mechanical equipment usually needs to work in a complex environment with strong background noise, so the mechanical vibration signals acquired on site are usually multi-component, non-stationary signals with background noise. Therefore, it becomes very diffic...

AI Technical Summary

Problems solved by technology

However, the current intelligent diagnosis algorithm still has the following two problems: (1) A large amount of training data is required. When the length of the input signal sequence is not enough to store information, these methods are often difficult to maintain a high enough accuracy, resulting in the comparison of diagnostic results. one-sided

(2) The training data needs to be preprocessed, resulting in a large manual workload and affecting the efficiency of diagnosis

These issues limit the flexibility of the diagnostic approach

Images