LSTM deep learning model-based hydropower unit fault diagnosis method and system

Abstract

The invention discloses an LSTM deep learning model-based hydropower unit fault diagnosis method and system. The method comprises the steps of obtaining sampling sequences of N different signal channels of a hydropower unit, and performing VMD on each time sequence to obtain K IMF components; constructing corresponding training sets and to-be-diagnosed sets; building an LSTM model for the trainingset of each IMF component, and performing feature extraction on each IMF component through two LSTM layers; connecting outputs of K LSTM layers of the same signal channel to a Dense layer; through aSoftmax layer, performing feature classification on outputs of multiple Dense layers; and training a deep learning neural network model through an RMSProp gradient descent algorithm, and diagnosing the to-be-diagnosed sets by the trained model. According to the method and the system, the relatively good signal-noise separation effect of the VMD is combined with the processing advantage of an LSTMto the time sequences, so that the hydropower unit fault diagnosis accuracy is effectively improved.

Description

[0001] The invention belongs to the technical field of fault diagnosis of hydroelectric units, and more specifically relates to a method and system for fault diagnosis of hydroelectric units based on an LSTM deep learning model. Background technique [0002] For a long time, the regular planned maintenance system has played an important role in the normal operation of hydropower stations, but this maintenance system cannot meet the growing demand for equipment maintenance, and the cost of maintenance is still very expensive. Driven by social needs and science and technology, the rapid development of fault diagnosis technology and systems provides the possibility for real-time monitoring and maintenance of hydroelectric units based on their operating status. very necessary. [0003] With the development of machine learning, pattern recognition, signal processing, artificial intelligence, etc., diagnostic methods based on data analysis and processing have begun to develop. This ...

AI Technical Summary

Problems solved by technology

However, due to the relatively shallow model structure of these traditional data-based fault diagnosis methods, they face problems such as the curse of dimensionality and limited learning ability for complex nonlinear objects as the detection data becomes larger and larger. method is insufficient in the face of modern complex equipment fault diagnosis problems, so there is an urgent need for a new method to adapt to changes in diagnostic requirements

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