Self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method

Abstract

The invention relates to a self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method, which can be applied to the fields, such as economic management abnormity detection, image recognition analysis, video retrieval, audio retrieval, signal abnormity detection, safety detection, and the like. The system comprises the following seven parts: an acquisition device, a transmitter device, an A / D (Analog / Digital) conversion device, a self-adaption wavelet neural network abnormity detection and fault diagnosis classification processor, a display interaction device, an abnormity alarm device and an abnormity processing device. The abnormity detection and fault diagnosis classification object of the self-adaption wavelet neural network abnormity detection and fault diagnosis classification system is acquired from samples for which a self-adaption mechanism is automatically established by the self-adaption wavelet neural network of a system to be detected, the characteristic information of a signal can be effectively extracted through wavelet transform multi-scale analysis, and a more accurate abnormity detection and fault diagnosis locating result can be obtained. The device adopting the method has the advantages of generalization, high accuracy in the application field, capability of real-time monitoring and low cost.

Description

technical field [0001] The invention proposes an adaptive wavelet neural network anomaly detection fault diagnosis classification system and method, which can be applied to abnormal detection, fault diagnosis, pattern recognition, industry, economic management, image, video, audio, signal processing, security and other fields. Background technique [0002] At present, the application requirements of anomaly detection and fault diagnosis are extremely extensive. For example, in the field of industrial control, many devices have to send out alarms when they are working abnormally, so that administrators can take timely measures; in the field of construction, construction workers urgently need a Diagnosis methods and equipment analyze faulty equipment and systems, and make fault location. In economic management, a method for analyzing economics-related data is needed, which can analyze and locate abnormalities and problems in a timely manner. In the field of image and video pr...

AI Technical Summary

Problems solved by technology

Even if there are some primary anomaly detection and fault diagnosis methods in a special field, such as template comparison method, general neural network method, and probability method, they all have application limitations, poor adaptability, and low accuracy (misdiagnosis and misdiagnosis). High judgment rate) and the problem of heavy dependence on sample data

And there is no analysis system for multi-domain anomaly detection, fault diagnosis and classification

Even if there is a dedicated fault analyzer in a specific field, the requirements for the detection object are very strict when used, especially the lack of adaptive ability to the input and output of the system, and the price is expensive

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