Health Monitoring of an Aircraft Fuel System Using Artificial Intelligence Techniques

Aircraft is a non-linear complex system and is need of regular monitoring. Integrated Vehicle Health Management (IVHM) is a process of health management paradigm, which involves system parameter monitoring, assessment of current, future conditions through diagnostic and prognostic approaches by providing required maintenance activities. Deployment of diagnostic, prognostic and health management processes enable to improve the system reliability and reduces the operating cost of the aircraft. Health monitoring and management plays a vibrant role in safe operation and maintenance of aircraft. Soft computing methodologies such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to estimate the health status of fuel system by developing model of a typical pump feed, twin-engine, four-tank small aircraft fuel system using Simulink in the laboratory environment. The controller is designed to generate the signals of the fuel tanks based on the fuel requirement of the engine. The ANFIS based management system helps to detect the faults existing in the fuel system and diagnose those faults using the expert’s logical rules. During a fault ailment, the controller’s performance is evaluated. The efficacy of this intelligent controller is verified with the present fuel control system and ANN controller

Effect of a Grain Refiner Cum Modifier on Mechanical Properties of Al-7Si and Al-11Si Alloys

This study evaluates the influence of grain refiners/modifiers on the mechanical properties of the Al-7Si and Al-11Si alloys with an experiment of quantitative and qualitative correlations with the microstructure. Modification of Al-Si alloys with strontium additions and grain refinement with Al-Ti, Al-B and Al-T-B master alloy additions are demonstrated to be efficient on Al-Si alloys. A single master alloy with combined additions of Sr and Ti and/or B was prepared and the microstructure and mechanical properties were studied. The results show that boron rich (Al-3B-Sr and Al-1Ti-3B-Sr) master alloys are more efficient than Ti rich (Al-3Ti-Sr and Al-5Ti-1B-Sr) master alloys considering their combined grain refinement and modification effect on Al-7Si and Al-11Si alloys. However, the presence of Sr does not influence the grain refinement. Similarly, presence of grain refiner does not influence the modification of eutectic Si

ANFIS-based fault diagnosis tool for a typical small aircraft fuel system

In the present paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent diagnosis tool for investigating the health of a typical small aircraft fuel system simulation was proposed. The system was designed for identifying the faults present in the aircraft fuel system and to diagnose those conditions with a proper fuel flow to the engine. The ANFIS intelligent tool works based on the logical rules of an expert system, which are developed as per the engine’s fuel consumption and the fuel flow from the tanks. The inputs to train the ANFIS are the fuel flow at the previous instant and the engine’s fuel consumption and the corresponding target is the fuel tank’s control signals. Training of ANFIS, generates the control signals as per the fuel requirement of the engine and the fuel flow to the tanks. The proposed intelligent controller model was implemented in the platform of MATLAB/Simulink and a comparison with the other techniques allowed the effectiveness of the proposed model

Health management of a typical small aircraft fuel system using an adaptive technique

Faults in the aircraft fuel system will degrade its performance and may lead to the complete system failure. In commercial aircraft system, efficient diagnosis can optimize the time to return the aircraft to service, thus allowing less disruption to passenger travel. In this work, an adaptive fault diagnosis technique is developed for a typical small aircraft fuel system, which facilitates efficient learning procedure to forecast the system parameters for non-linear situations. This adaptive technique represents the integration of the Fuzzy Logic and Support Vector Machine (SVM) algorithms in the field of fault diagnosis. Using this adaptive technique health monitoring of aircraft fuel system is discussed. In an aircraft fuel tank, the fault is effectively located by assessing and contrasting the actual parameters and set point parameters related to the system for various time-frames. The fuzzy logic controller is configured with the logical rules as per the required target output. It relies on the aircraft fuel system parameters like the fuel flow rate, level of fuel in the tank, fuel temperature, and fuel pressure. From the logical rules, the control signals related to the aircraft fuel system are derived by the SVM technique. The efficiency in execution of this fault diagnosis tool-based aircraft fuel system gets authenticated in the MATLab/Simulink platform. The simulation is carried by assuming normal operating conditions of aircraft in the laboratory environment