A Novel UKF-RBF Method Based on Adaptive Noise Factor for Fault Diagnosis in Pumping Unit
| Autor: | Wei Zhou, Haibo He, Jun Yi, Li Xiaoliang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science 020208 electrical & electronic engineering Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology Kalman filter Noise figure Fault (power engineering) Fault detection and isolation Computer Science Applications Adaptive filter Noise Nonlinear system Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Radial basis function Electrical and Electronic Engineering Algorithm Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 15:1415-1424 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2018.2839062 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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