Sensor fault detection and isolation of an industrial gas turbine using partial block-wise adaptive kernel PGA

2017 ◽  
Author(s):  
Mania Navi ◽  
Mohammadreza Davoodi ◽  
Nader Meskin
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Adaptive Kernel ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

scholarly journals Fault Detection and Isolation Based on Dynamic Observers Applied to Gas Turbine Control Sensors

1998 ◽  
Author(s):  
S. Simani ◽  
P. R. Spina ◽  
S. Beghelli ◽  
R. Bettocchi ◽  
C. Fantuzzi
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Actual System ◽  
Analytical Redundancy ◽  
Residual Functions ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In order to prevent machine malfunctions and to determine the machine operating state, it is necessary to use correct measurements from actual system inputs and outputs. This requires the use of techniques for the detection and isolation of sensor faults. In this paper an approach based on analytical redundancy which uses dynamic observers is suggested to solve the sensor fault detection and isolation problem for a single-shaft industrial gas turbine. The proposed technique requires the generation of classical residual functions obtained with different observer configurations. The diagnosis is performed by checking fluctuations of these residuals caused by faults.

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 Integral Sensor Fault Detection and Isolation for Railway Traction Drive

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1543 ◽  
Author(s):  
Fernando Garramiola ◽  
Jon del Olmo ◽  
Javier Poza ◽  
Patxi Madina ◽  
Gaizka Almandoz
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Traction Drive ◽  
Railway Traction ◽  
Sensor Fault Detection

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Sensor Fault ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

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