Name
Title | ||
|---|---|---|
A Novel UKF-RBF Method Based on Adaptive Noise Factor for Fault Diagnosis in Pumping Unit |
Abstract | ||
|---|---|---|
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 (
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$w_{m,l}$</tex-math></inline-formula>
), the center (
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c_{m}$</tex-math></inline-formula>
), and the width (
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b_{m}$</tex-math></inline-formula>
) 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TII.2018.2839062 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Fault diagnosis,Feature extraction,Adaptation models,Production,Neural networks,Shape,Informatics | Polygon,Radial basis function,Nonlinear system,Computer science,Fault detection and isolation,Algorithm,Noise figure,Control engineering,Kalman filter,Adaptive filter,Artificial neural network | Journal |
Volume | Issue | ISSN |
15 | 3 | 1551-3203 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhou | 1 | 193 | 28.72 |
| Xiaoliang Li | 2 | 28 | 7.12 |
| Jun Yi | 3 | 27 | 7.67 |
| Haibo He | 4 | 3653 | 213.96 |