scholarly journals A self-validating control system based approach to plant fault detection and diagnosis

2001 ◽  
Vol 25 (2-3) ◽  
pp. 337-358 ◽  
Author(s):  
Jun Chen ◽  
John Howell
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA

2014 ◽  
Vol 623 ◽  
pp. 202-210
Author(s):  
Ping Xu ◽  
You Cai Wang ◽  
Kai Wang ◽  
Qiu Yan Wang
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Principal Component ◽  
Kernel Functions ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Practical Result ◽  
Complex Control ◽  
Complex Control System ◽  
Detection And Diagnosis

The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four common sensor faults. At first its fault is detected by Q statistic; secondly its fault is identified by T2 contribution percent change. The simulation and the practical result show the KPCA method has good performance on complex control system in sensor fault detection and diagnosis.

scholarly journals Fault detection and isolation in an aircraft engine bleed air system

2021 ◽  
Author(s):  
Lan Shang
Keyword(s):  
Control System ◽  
Heat Exchanger ◽  
Fault Detection ◽  
Air Temperature ◽  
Temperature Control ◽  
Fault Detection And Diagnosis ◽  
Temperature Control System ◽  
Control Command ◽  
Detection And Diagnosis ◽  
Valve Control

High temperature pressurized bleed air generated in the engine compressor is used for air conditioning in a jetliner. The engine bleed air must be regulated to a target temperature in the bleed air temperature control system using cold ram air before it can be used in the cabin or other low temperature area. The bleed air system is a control system that consists of sensors, and valve actuators. Potential faults associated with these components need to be considered in the design of the control system. This dissertation focuses on fault detection and diagnosis of the bleed air temperature control system. The faults in temperature sensors and valve actuators are detected using two unscented Kalman filters. The source of a fault is identified using the squared residuals. Nonlinear governing equations for the engine bleed air temperature control system are derived in state space form. Convergence analysis of the proposed fault detection and diagnosis methods is conducted. Heat exchanger is another core component in the bleed air temperature control system. A common fault associated with a heat exchanger is fouling which reduces the heat transfer efficiency, introduces additional resistance to flow, and increases fuel consumption. This dissertation presents a heat exchanger fouling detection method based on the valve control command of an engine bleed air temperature control system. Heat exchanger fouling is monitored by estimating the deviation of valve control command. A simulated bleed air temperature control system has been designed and built. Experiments have been conducted for temperature sensor fault, valve actuator fault, and heat exchanger fouling. Computer simulations for each of these cases are conducted and compared to experimental results.

scholarly journals Fault detection and isolation in an aircraft engine bleed air system

2021 ◽  
Author(s):  
Lan Shang
Keyword(s):  
Control System ◽  
Heat Exchanger ◽  
Fault Detection ◽  
Air Temperature ◽  
Temperature Control ◽  
Fault Detection And Diagnosis ◽  
Temperature Control System ◽  
Control Command ◽  
Detection And Diagnosis ◽  
Valve Control

High temperature pressurized bleed air generated in the engine compressor is used for air conditioning in a jetliner. The engine bleed air must be regulated to a target temperature in the bleed air temperature control system using cold ram air before it can be used in the cabin or other low temperature area. The bleed air system is a control system that consists of sensors, and valve actuators. Potential faults associated with these components need to be considered in the design of the control system. This dissertation focuses on fault detection and diagnosis of the bleed air temperature control system. The faults in temperature sensors and valve actuators are detected using two unscented Kalman filters. The source of a fault is identified using the squared residuals. Nonlinear governing equations for the engine bleed air temperature control system are derived in state space form. Convergence analysis of the proposed fault detection and diagnosis methods is conducted. Heat exchanger is another core component in the bleed air temperature control system. A common fault associated with a heat exchanger is fouling which reduces the heat transfer efficiency, introduces additional resistance to flow, and increases fuel consumption. This dissertation presents a heat exchanger fouling detection method based on the valve control command of an engine bleed air temperature control system. Heat exchanger fouling is monitored by estimating the deviation of valve control command. A simulated bleed air temperature control system has been designed and built. Experiments have been conducted for temperature sensor fault, valve actuator fault, and heat exchanger fouling. Computer simulations for each of these cases are conducted and compared to experimental results.

Design of a fault detection and diagnose system for intelligent unmanned aerial vehicle navigation system

2018 ◽  
Vol 233 (6) ◽  
pp. 2170-2176 ◽  
Author(s):  
Qian Zhang ◽  
Xueyun Wang ◽  
Xiao Xiao ◽  
Chaoying Pei
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Fuzzy System ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Aerial Vehicles ◽  
Detection And Diagnosis

A secure control system is of great importance for unmanned aerial vehicles, especially in the condition of fault data injection. As the source of the feedback control system, the Inertial navigation system/Global position system (INS/GPS) is the premise of flight control system security. However, unmanned aerial vehicles have the requirement of lightweight and low cost for airborne equipment, which makes redundant device object unrealistic. Therefore, the method of fault detection and diagnosis is desperately needed. In this paper, a fault detection and diagnosis method based on fuzzy system and neural network is proposed. Fuzzy system does not depend on the mathematical model of the process, which overcomes the difficulties in obtaining the accurate model of unmanned aerial vehicles. Neural network has a strong self-learning ability, which could be used to optimize the membership function of fuzzy system. This paper is structured as follows: first, a Kalman filter observer is introduced to calculate the residual sequences caused by different sensor faults. Then, the sequences are transmitted to the fault detection and diagnosis system and fault type can be obtained. The proposed fault detection and diagnosis algorithm was implemented and evaluated with real datasets, and the results demonstrate that the proposed method can detect the sensor faults successfully with high levels of accuracy and efficiency.

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