Fault detection and diagnosis in the pumping unit is a challenging industrial problem for the system that exhibits nonlinearity, coupled parameters, and time-varying noise. This paper proposes a novel combined unscented Kalman filter (UKF) and radial basis function (RBF) method based on an adaptive noise factor for fault diagnosis in the pumping unit. First, to reduce computation and complexity of the diagnosis model, the Fourier descriptor method based on an approximate polygon is presented to extract the features of the indicator diagram. RBF neural network is adopted to establish the fault diagnosis model based on indicator diagram data and production data. In particular, UKF is used to train the weights (w m,l ), the center (c m ), and the width (b m ) of the RBF model. Furthermore, the adaptive noise factor method is proposed to address the adaptive filtering issue in the fault diagnosis model. The proposed method is applied to the pumping unit system, and experimental results show the effectiveness and favorable recognition rate in classifying multiple faults.

中文翻译：

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基于自适应噪声因子的抽油机故障诊断UKF-RBF新方法


对于具有非线性、耦合参数和时变噪声的系统来说，抽油机的故障检测和诊断是一个具有挑战性的工业问题。本文提出了一种基于自适应噪声因子的无迹卡尔曼滤波器（UKF）和径向基函数（RBF）相结合的新型抽油机故障诊断方法。首先，为了减少诊断模型的计算量和复杂度，提出了基于近似多边形的傅里叶描述子方法来提取指示图的特征。采用RBF神经网络根据示功图数据和生产数据建立故障诊断模型。具体来说，UKF 用于训练 RBF 模型的权重 (wm,l )、中心 (cm ) 和宽度 (bm )。此外，提出了自适应噪声因子方法来解决故障诊断模型中的自适应滤波问题。将该方法应用于抽油机系统中，实验结果表明该方法对多故障分类是有效的，且具有良好的识别率。