Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults

2016 ◽  
Vol 24 (5) ◽  
pp. 1058-1071 ◽  
Author(s):  
Huimin Wang ◽  
Guang-Hong Yang ◽  
Dan Ye
Keyword(s):  
Fault Detection ◽  
Fuzzy Systems ◽  
Fault Detection And Isolation ◽  
Sensor Faults

scholarly journals Design of a Fault Detection and Isolation System for Intelligent Vehicle Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Wei Huang ◽  
Xiaoxin Su
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Intelligent Vehicle ◽  
Sensor Faults ◽  
Driver Assistance ◽  
Isolation System ◽  
Extended Kalman Filters ◽  
Vehicle Navigation System ◽  
Fault Sensitivity ◽  
Sensitivity Pattern

This paper deals with the design of a fault detection and isolation (FDI) system for an intelligent vehicle, a vehicle equipped with advanced driver assistance system (ADAS). The ADASs are outfitted with sensors for acquiring various information about the vehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed FDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies a generalized observer scheme (GOS). In the GOS, there is bank of extended Kalman filters (EKFs), each excited by all except one sensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults but one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The designed FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with diagnosing individual sensor faults and outputting fault-free vehicle states.

Fuzzy robust fault estimation scheme for fault tolerant flight control systems based on unknown input observer

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gulay Unal
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fault Detection ◽  
Flight Control ◽  
Fault Tolerant ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Flight Control System ◽  
Content Type

Purpose The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely important in obtaining healthy flight. An approach to detect and isolate aircraft sensor faults is proposed, and a new integrated structure for a fault tolerant aircraft control system is presented. Design/methodology/approach As disturbance and sensor faults are mixed together in a flight control system, it is difficult to isolate any fault from the disturbance. This paper proposes a robust unknown input observer for state estimation and fault detection as well as isolation using fuzzy logic. Findings The dedicated observer scheme (DOS) and generalized observer scheme (GOS) are used for fault detection and isolation in an observer-based approach. Using the DOS, it has been shown through simulation that sensor fault detection and isolation can be made, but here the threshold value must be well chosen; if not, the faulty sensor cannot be correctly isolated. On the other hand, the GOS is more usable and flexible than the DOS and allows isolation of faults more correctly and for a fuzzy logic-based controller to be used to realize fault isolation completely. Originality/value The fuzzy logic approach applied to the flight control system adds an important key for sensor fault isolation because it reduces the effect of false alarms and allows the identification of different kinds of sensor faults. The proposed approach can be used for similar systems.

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.

scholarly journals Fault Detection and Isolation for a Cooling System of Fuel Cell via Model-based Analysis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1115
Author(s):  
Jaesu Han ◽  
Sangseok Yu ◽  
Jaeyoung Han
Keyword(s):  
Fuel Cell ◽  
Fault Detection ◽  
Linear Models ◽  
Cooling System ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Model Based ◽  
The Difference ◽  
Cumulative Sum Control ◽  
Cusum Method

The development of fuel cell electric vehicles in recent years has led to increased interest in the use of fuel cells as sources of renewable energy. To achieve successful commercialization of fuel cell vehicles, it will be necessary to guarantee the safety, reliability, and lifetime of fuel cell systems by predictive fault detection and isolation (FDI). In this study, the parity equation, an observer, and a Kalman filter are employed together to compare the characteristics of FDI, focusing on the sensors of the thermal management system. Residuals corresponding to the difference between temperature outputs of linear models under driving cycles and nonlinear temperature outputs are used to isolate faults. Then, assessment of three model-based sensor FDI schemes is used to isolate sensor faults using the Cumulative Sum Control Chart (CUSUM) method. Generated residuals are evaluated by CUSUM to detect the presence of a sensor fault. As a result, isolated sensor faults are assessed.

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