The research problem for Structural Health Monitoring (SHM) is proposed to be addressed through identifying methods consisting of the state of the art modeling technique, development of the neural network model, training the model and building adequate robustness into the model through the computational research work being undertaken.
The activity in SHM has primarily two parts. The first one refers to damage identification and the second part deals with damage growth or prognosis to determine the remaining life of a damaged component using computational techniques. Besides the airframe of an aircraft, Gas Turbine engines are safety-critical systems that require continuous monitoring of the state of the system to avoid catastrophic failures.
The total work on SHM consists of the acquisition of data from aircraft in terms of the loads and carryout prognostic analysis to predict damage growth to estimate the remaining life of the structure as a whole and for critical components.
The convergence of technologies in the field of Command, Communication, Computers, Control, and intelligence (C4I) resulted in the emergence of the C4I system. The basic purpose of the C4I system is to destroy the enemy targets completely and thus paralyze the enemy. In order to expose and destroy camouflaged effects of adverse weather conditions, multi-sensor inputs are required to be coordinated and processed. A control center of a typical C4I system with facilities to identify the potential targets by acquiring multi-sensor inputs and processing these inputs using data fusion and image processing technique will be simulated. The research work envisaged involve concept development of sensor fusion and C4I system and associated embedded systems.
The major objectives of the research work involves, the schemes of fusion that can be investigated are:
The investigation on C4I system development comprises advanced visualization capabilities, analytic tools, knowledge management and database design.
Gas Turbine engines are safety-critical systems that require continuous monitoring of the state of the system to avoid catastrophic failures. There are several techniques developed for ensuring the flight safety. Condition Based Monitoring is one such technique for ensuring flight safety. In the context of a typical aero engine, a research project as given below has been formulated.
Develop a state of the art diagnostic neural network model for an engine utilizing the data captured through the engine performance deck and the engine test data.
The above-stated research problem is proposed to be addressed through literature survey to identify the state of the art modeling technique, development of the neural network model, training the model and building adequate robustness into the model through the research work being undertaken.
Development of support structure to air traffic management (ATM) using unmanned air system (UAS) platforms. This is a potential area for intensive research pertaining to Aviation safety.
Global and local (or reactive) obstacle avoidance across unmanned aerial vehicle (UAV) altitudes is a challenging problem. When a camera vision is available, techniques to determine obstacle positions in an inertial frame are available. Hence a requirement in vertical (pitch) plane is to plan vehicle positions different from these obstacle positions. A challenge in this problem setup is that the nonlinear behavior of UAV is so unpredictable that planning such vehicle positions in reactive obstacle positions becomes extremely difficult. However, in global obstacle avoidance, when the obstacle positions in configuration space are available a priori, UAV positions that avoid the obstacles are synthesized offline.
The second part is the path planning scheme as and when an obstacle position is sensed. This is the preliminary work required for reactive obstacle avoidance problem. The whole gamut of path planning schemes addressed without integrating them with the vehicle dynamics will be taken into consideration.