用声振动信号实时诊断天然气泵机组故障，准确率达90%以上。
Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
- 直接用真实声振数据作为神经网络输入，避免仿真误差。
- 三种工况分类准确率分别达100%、98.5%和90.9%。
- 适用于各类功率泵机组，适合工程实时监测场景。
近年来，人工神经网络（ANN）在天然气泵机组（GPU）故障诊断中的应用日益受到关注。传统方法依赖模拟故障数据训练模型，但无法反映真实运行状态。本文提出采用实际运行中产生的声学与振动信号特征作为神经网络输入。对意大利诺沃皮尼翁公司生产的GTK-25-i型泵机组的实测声振信号进行了描述性统计分析，并提取了五组最大幅值分量及每样本的标准差作为诊断特征，实时输入神经网络。基于TensorFlow、Keras、NumPy、pandas等框架，在Python 3环境下构建深度全连接前馈神经网络，采用误差反向传播算法进行训练。测试结果显示：1475个信号样本中，“正常”状态分类精度为1.0000，“运行中”状态为0.9853，“故障”状态为0.9091。该模型可有效识别各类泵机组技术状态，满足实际应用需求，有助于预防故障发生。该方法适用于任意类型和功率的泵机组。
原文摘要 · Abstract (English)
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.
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