Fault prediction method based on fast integrated convolutional neural network

Abstract

The invention belongs to the technical field of neural network fault prediction, and discloses a fault prediction method based on a fast integrated convolutional neural network. The method comprises the following steps: (a) collecting an output signal of a to-be-predicted object, and carrying out time-frequency analysis on the output collected signal by adopting S transformation to obtain a time-frequency signal; (b) constructing a convolutional neural network structure based on LeNet-5, providing a maximum and minimum cosine cyclic learning rate scheduler based on a self-weight starting cosine cyclic learning rate, and setting a learning rate range of the convolutional neural network; and (c) training a time-frequency signal of the prediction data by using the convolutional neural network, and taking a prediction result of the SECNN as a final prediction result. According to the invention, fault prediction with high prediction speed and high prediction precision can be realized.

Description

technical field [0001] The invention belongs to the technical field of neural network fault prediction, and more specifically relates to a fault prediction method based on fast integrated convolutional neural network. Background technique [0002] Fault diagnosis plays a vital role in modern complex systems. Data-driven fault diagnosis methods have been increasingly studied by many researchers from academic and engineering fields. Advanced data-based methods to effectively mine data features and accurately predict fault modes have become the key research directions of data-driven fault diagnosis. These are based on Data-based methods can use historical data to build failure models without relying on previous physical models, making them well suited for modern complex industrial processes. [0003] Deep learning (DL) uses data representation learning instead of explicit feature extraction to perform learning tasks, and enables features to be learned automatically, showing gr...

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Problems solved by technology

[0004] Almost all ensemble learning methods need to train multiple base learners, and the training process is very time-consuming, so a new ensemble learning is proposed, called Fast Ensemble Learning (SEL), which uses Cosine Cyclic Learning Rate (CCLR) scheduling , and divide the entire training process into M cycles. Since the training model will reach local minima at the end of each cycle, SEL combines these local minima and shows strong performance. SEL trains the model once Obtaining M different base learners overcomes the above disadvantages. However, how to choose the appropriate learning rat

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