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

Active Publication Date: 2018-06-22
HUAZHONG UNIV OF SCI & TECH
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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 a

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

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[0071] Example 1:

[0072] 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:

[0073] 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.

[0074] 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 Embodiment

[0133] Example 2:

[0134] 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:

[0135] 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;

[0136] 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;

[0137] 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;

[0138] 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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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.

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Owner HUAZHONG UNIV OF SCI & TECH
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