Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

2018 ◽  
Vol 22 (2) ◽  
pp. 624-647 ◽  
Author(s):  
Hamed Habibi ◽  
Ian Howard ◽  
Reza Habibi
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Probability Estimation ◽  
Sensor Fault ◽  
Fault Probability ◽  
Sensor Fault Detection

scholarly journals A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3580 ◽  
Author(s):  
Hong Wang ◽  
Hongbin Wang ◽  
Guoqian Jiang ◽  
Yueling Wang ◽  
Shuang Ren
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Temporal Correlation ◽  
Deep Belief Network ◽  
Stable Operation ◽  
Sensor Fault ◽  
Temporal Characteristics ◽  
Belief Network ◽  
Sensor Fault Detection ◽  
Spatio Temporal

Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

scholarly journals Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation

2020 ◽  
Vol 53 (2) ◽  
pp. 86-91
Author(s):  
Benjamin Jahn ◽  
Michael Brückner ◽  
Stanislav Gerber ◽  
Yuri A.W. Shardt
Keyword(s):  
Fault Detection ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Parity Space ◽  
Residual Generation
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