Early fault diagnosis method for complex equipment

Abstract

The invention discloses an early fault diagnosis method for complex equipment. The early fault diagnosis method for the complex equipment comprises the specific steps: extracting collection signals through a sensor; carrying out wavelet transform on the extracted collection signals for de-noising; and establishing a fault diagnosis model based on a BP (Back Propagation) neural network and a fault diagnosis model based on an SVM (Support Vector Machine) according to mechanical features and circuit features, and carrying out fault diagnosis. A kernel function in the fault diagnosis model based on the SVM is a polynomial kernel function, a radial basis function (RBF) or a Sigmoid kernel function. According to the early fault diagnosis method, the fault diagnosis of the complex equipment is divided into a fault diagnosis with the mechanical features and a fault diagnosis with the circuit features, and the fault diagnosis models are established respectively according to the mechanical features and the circuit features, so that mechanical tests are easy, many samples can be obtained, quick convergence can be realized by using the BP neural network, and accuracy is higher; furthermore, the circuit sample data is less. With the adoption of the advantages of small samples of the SVM, the fault diagnosis of the complex system, such as the complex equipment, can be realized.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to an early fault diagnosis method for complex equipment. Background technique [0002] Fault diagnosis mainly refers to the monitoring of equipment status and judgment of faults. It not only needs to make correct judgments on the cause, location, and degree of equipment failures, and then prevent them and reduce failure losses. It also needs to monitor the health status of equipment, especially For the monitoring of early faults, early warning and judgment can be carried out as soon as possible to reduce the occurrence of sudden faults, and it can also predict the time of future faults, which can fully save maintenance resources and provide the best maintenance decision-making basis for equipment maintenance and maintenance. It provides the possibility to realize the maintenance strategy. [0003] Technically speaking, fault diagnosis is actually under the guidance of a certain fault ju...

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Problems solved by technology

[0005] For the detection of a single signal, it is easy to establish its interval range. If the complex system has multiple monitoring signals, it is difficult to establish a specific envelope model, and it is difficult to use this method for fault diagnosis.

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