Adaptive fault diagnosis method based on one-dimensional convolutional neural network

Abstract

The invention discloses an adaptive fault diagnosis method based on a one-dimensional convolutional neural network. According to the adaptive fault diagnosis method, the one-dimensional convolutionalneural network is used as a main model structure; a high-precision diagnosis result can be output immediately by directly inputting a one-dimensional time sequence signal; compared with a traditionalmode of diagnosis by manual feature extraction, the adaptive fault diagnosis method provided by the invention is more convenient and efficient and has lower requirements for professional knowledge andexperience of diagnosis personnel; meanwhile, compared with the mode of fault diagnosis based on neural network structures, the one-dimensional convolutional neural network model is simpler and faster in calculation while maintaining the diagnosis precision, and can provide good guarantee for real-time fault diagnosis. The real-time and rapid adaptive fault diagnosis method provided by the invention can deal with the diversity of fault types easily, and can be widely applied to the fields of machine manufacturing, aerospace, electricity, metallurgy and the like.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and computer artificial intelligence, in particular to a method for fault diagnosis of mechanical equipment and parts based on artificial intelligence convolutional neural network (CNN). Background technique [0002] Existing mechanical equipment and parts are developing in the direction of high speed, high efficiency, and precision. However, in actual operation, the working conditions are complicated, and it is often necessary to analyze and troubleshoot equipment performance and potential faults to achieve early detection, Early detection, early avoidance. Among them, key components of mechanical equipment such as gears, bearings, compressor valves, and motors are often the key objects in the field of mechanical fault diagnosis and monitoring. In this field, traditional time-frequency analysis and other methods have achieved good results in small data environments and single-fault tasks...

AI Technical Summary

Problems solved by technology

When many devices are running, a second late diagnosis will bring incalculable losses. Every second must be counted, and the operating status of the diagnostic devices can be detected and diagnosed in a timely and accurate manner.

Images