Abstract
The reciprocating pump plays an important role in the petrochemical industry procedure, it is crucial in ensuring the systematic safety and stability. Since the useful feature information of the vibration signal from the reciprocating pump tends to be overwhelmed by the background ingredients, it is tough to realize the recognition on typical modes. Aiming at the extraction of reciprocating mechanical fault features and mode recognition, this paper proposes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and LSTM (Long Short-Term Memory) deep neural network algorithm. Firstly, the IMF components are obtained by decomposing the vibration signals from the reciprocating pump with the Improved CEEMDAN algorithm, in which the key parameter β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> is improved and redefined, for optimizing SNRs (Signal Noise Ratio) of the IMF (Intrinsic Mode Function) components. Then the corresponding singular spectral entropy is calculated and the feature vector is constructed. The classification modal based on LSTM deep network is developed in the data dividing-training and the final mode recognition process. The study shows that the proposed method can effectively extract the fault features of vibration signal of the reciprocating pump, and the testing modes could be accurately recognized with the developed classification model.
Highlights
Reciprocating pump is an important industrial equipment in the field of petroleum, water supply and drainage systems
Aiming at the fault diagnosis method for key parts of reciprocating pump, a fault feature extraction method based on CEEMDAN, singular spectral entropy and LSTM deep neural network algorithm is proposed, in which the dynamic analysis on fault modes of typical reciprocating pump is developed as the primary point, the diagnosis model is put forward with key parameters calculation and accuracy comparison
An improved simulation and analysis method based on the CEEMDAN and LSTM has been proposed in order to accurately extract the typical characteristics and identify the fault patterns of the reciprocating pump
Summary
Reciprocating pump is an important industrial equipment in the field of petroleum, water supply and drainage systems. For the CEEMDAN algorithm proposed by Torres M E proposed, the adaptive white noise is added to each stage of the decomposition and the modal components by calculating the residuals are obtained, thereby the process improves computational efficiency by reducing modal aliasing [7]. CEEMDAN, singular spectral entropy and LSTM deep neural network algorithm is proposed, in which the dynamic analysis on fault modes of typical reciprocating pump is developed as the primary point, the diagnosis model is put forward with key parameters calculation and accuracy comparison. IMPROVED CEEMDAN ALGORITHM By adding positive and negative white noise to each IMF component, the error of IMF reconstruction could be greatly reduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is considered as a significant improvement of EEMD It has been widely used in fields of fault diagnosis, seismology, building energy consumption, etc. That the white noise energy and the IMF component energy remain the same dimension when mixed with white noise, maintaining a stable SNR
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