Ph.D(ERP). Thesis Colloquium


Ph.D(ERP). Thesis Colloquium 

NAME OF THE CANDIDATE   : Mr. Jineesh Thomas 

DEGREE                                       : Ph.D. (ERP) 

TITLE OF THE THESIS            : Shape and Load Estimation using Fiber Bragg              

                                                          Grating Sensors for Structural Health  

                                                           Monitoring of Aircraft Structures 

SUPERVISORS                            : Prof. S. Asokan & Dr. T.R. Rajanna (HAL) 

DATE & TIME                             : Thursday, 10th December 2020 at 11:00 AM. 

VENUE                                          : Online (Microsoft team) Link 




Aerospace structures are exposed to severe loading and environmental conditions which necessitate constant inspection and maintenance to improve the safety and reliability. On-board Structural Health Monitoring (SHM) allows continuous inspection even when the aircraft is in service, reducing maintenance costs and downtime. Fiber optic sensor technology, in particular, Fiber Bragg Grating (FBG) sensors have become increasingly popular for SHM applications of aerospace engineering due to its unique superior characteristics in terms of small size, electromagnetic immunity, multiplexing capability, high bandwidth and the possibility to be embedded within the material. Under the scope of the investigations carried out in this thesis, FBG sensors have been explored for the shape estimation and load monitoring of aircraft structures.

A modal approach for shape estimation is investigated for the purpose of real time health monitoring, control and condition assessment of lightweight aerospace structures. The methodology implements the use of FBG sensors to obtain strain measurements from the target structure and to estimate the displacement field. A strain to displacement transformation matrix is derived using mode shapes, to estimate the global displacement of a structure from measured discrete strain data. The number of FBG sensors and sensor layout for the shape estimation are optimized using genetic algorithm. Static and dynamic displacement experiments are conducted on an aluminum plate to verify the algorithm. To test the performance of the algorithm for a large-scale application, the wing shape of an all-metal turboprop aircraft is estimated during static loading on ground. The results show that the proposed algorithm along with strain data measured using FBG sensors could estimate the real time shape of aerospace structures.

The second application demonstrates the feasibility of estimating the aircraft landing gear structural loads using strain data from FBG sensors and machine learning algorithm. Gaussian process regression is used for the prediction of loads on the landing gear components

such as axle, side brace, drag brace and shock strut. Using this method, the operational load on several components of landing gear can be estimated to high accuracy using strain measured from axle and common landing gear measurements such as wheel speed, shock absorber pressure, shock absorber travel and aircraft attitude, descent velocity and acceleration. To train the model comprehensive measurement data from drop tests are used. The load estimation results for unseen drop test data show that the proposed method can be used to predict the load pattern of aircraft landing gears.

Apart from 2D shape estimation, linear displacement measurement is important for the control and health monitoring applications of aerospace industry. In this work, an improved FBG based displacement sensor is designed for reliable operation in harsh environments. The calibration and field trail results demonstrate higher sensitivity with excellent linearity and temperature compensation. This sensor can be utilized for long-range and high endurance displacement measurement applications.





Date(s) - 10/12/2020
11:00 am - 12:30 pm

Teams-Microsoft Online
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