Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
This work addresses fault diagnosis for gas pumping units, which is an incremental application of existing neural network methods to a specific industrial domain.
The paper tackled fault diagnosis in natural gas pumping units by developing a feedforward neural network that uses acoustic and vibration signals as input, achieving high classification precision (e.g., 0.9091 for defective states) to prevent failures.
In recent years, more and more attention has been paid to the use of artificial neural networks (ANN) for diagnostics of gas pumping units (GPU). Usually, ANN training is carried out on models of GPU workflows, and generated sets of diagnostic data are used to simulate defect conditions. At the same time, the results obtained do not allow assessing the real state of the GPU. It is proposed to use the values of the characteristics of the acoustic and vibration processes of the GPU as the input data of the ANN. A descriptive statistical analysis of real vibration and acoustic processes generated by the operation of the GPU type GTK-25-i (Nuovo Pignone, Italy) has been carried out. The formation of packets of diagnostic signs arriving at the input of the ANN has been carried out. The diagnostic features are the five maximum amplitude components of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic signs are calculated directly in the input pipeline of ANN data in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedforward ANN, training on the error backpropagation algorithm. The results of training and testing of the developed ANN are presented. During testing, it was found that the signal classification precision for the "nominal" state of all 1475 signal samples is 1.0000, for the "current" state, precision equils 0.9853, and for the "defective" state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power.