Vibration signal-based rotating machinery fault direct intelligent diagnosis method

Abstract

The invention discloses a vibration signal-based rotating machinery fault direct intelligent diagnosis method. The method comprises the following steps of 1, sampling collected fault signals through an overlapping sampling layer; 2, segmenting samples through a convolutional layer; 3, training a sparse filter network subjected to L3 / 2 regularization through training data, obtaining a weight matrix, and extracting fault features of data segments through the weight matrix and a new activation function; 4, performing pooling dimension reduction on the features of the segments obtained by calculation through a pooling layer; 5, training an output layer, namely, a Softmax classifier by the pooled features, and classifying the extracted features of the samples; and 6, inputting the fault signalsto the trained network for performing diagnosis. The vibration signal-based rotating machinery fault direct intelligent diagnosis method realizes automatic extraction of the features and direct intelligent diagnosis of the fault signals, and is better in generalization capability and relatively high in accuracy and stability.

Description

technical field [0001] The invention relates to a vibration signal analysis technology of a rotating machine and a machine fault diagnosis technology, in particular to an automatic feature extraction technology directly used in the original signal of a rotating machine fault. Background technique [0002] Vibration signal is the carrier of mechanical fault characteristics. Analyzing the vibration signal of mechanical equipment, extracting fault features, and identifying faults are common methods for mechanical fault diagnosis. Mechanical equipment usually works in a working environment with multiple vibration sources, and the background noise is strong, so the mechanical vibration signal measured on site is usually a multi-component non-stationary signal under strong background noise. In this case, from the complex mechanical vibration signal It becomes difficult to separate mechanical vibration signals with similar failure modes. Therefore, in order to improve the accuracy...

AI Technical Summary

Problems solved by technology

In order to solve the problem of shift variability in the use of the original vibration signal; the edge problem in the original CNN network, etc.

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