LSTM deep learning model-based hydropower unit fault diagnosis method and system
A technology for hydroelectric generating units and fault diagnosis, applied in neural learning methods, motor generator testing, biological neural network models, etc. The effect of reduced computation, high efficiency and accuracy
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Problems solved by technology
 Example 1:
 The embodiment of the present invention proposes a method for diagnosing faults of a hydropower unit based on an LSTM deep learning model. Refer to figure 1 As shown in the diagnostic model diagram, the specific implementation steps are as follows:
 Step 1: Obtain the sampled values of the vibration signals of the N different signal channels of the hydropower unit. Variational modal decomposition is performed on the vibration signal of each signal channel, and the decomposed K IMF components are obtained.
 Among them, the premise of VMD decomposition is to construct a variational problem. Assuming that each ‘mode’ is a finite bandwidth with a center frequency, the variational problem can be described as seeking K IMF components u k (t), minimize the sum of the estimated bandwidth of each mode, and the constraint condition is that the sum of each mode is the original input signal. The construction process of the variational problem is as f...
 Example 2:
 The embodiment of the present invention also provides a fault diagnosis system of a hydropower unit based on the LSTM deep learning model, such as Figure 4 As shown, the system includes:
 The IMF component acquisition unit is used to acquire the sampling sequences of N different signal channels of the hydropower unit, and perform variational modal decomposition on each time sequence to obtain K IMF components;
 The training set and the set to be diagnosed acquisition unit are used to normalize each IMF component and construct the corresponding training set and set to be diagnosed;
 The feature extraction unit is used to construct a long and short-term memory network model for the training set of each IMF component, and perform feature extraction for each eigenmode function through at least two LSTM layers;
 The training module is used to connect the K LSTM layer outputs of the same signal channel to a Dense layer, and then co...
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