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Structural Health Monitoring using FBGs
Fiber Bragg grating based technique uses feature-guided waves to detect anomalies or defects in plate structures with transverse bends. Ongoing research is on Structure health monitoring (SHM) of composites, which plays a vital role in Aerospace industry. Defect identification is a part of SHM. Composites which are light weight and stiff may give little or no warning before failing. These material failures like delamination will cause severe damage. Using guided waves and embedding FBG in laminates will help to detect the material defects at earlier stage thereby avoiding severe damage. Interrogation with guided waves are attractive as it provides defect identification and localization. FBGs are widely adapted in SHM due to their rugged wavelength-encoded transduction mechanism, immunity to EM interference, ability to sense multiple parameters like temperature and strain, compact in size and less cost compared to conventional sensors.

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Reference:

P. Ray, P. Rajagopal, B. Srinivasan, K. Balasubramaniam, Feature Guided Wave–Based Health Monitoring of Bent Plates Using Fiber Bragg Gratings, Journal of Intelligent Material Systems and Structures, 28, 1211-1220 (2017)

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Detection of partial discharges in power transformers using FBG sensors
The primary goal of any power distribution network is to guarantee uninterrupted power supply to the consumer. One of the critical components in such a power distribution network is the power transformer which is susceptible to breakdown primarily due to improper insulation design or poor quality of insulation structure or casing. As such, condition monitoring of transformer insulation forms an integral step in ensuring consistent operation of the power equipment. Partial discharges (PD), which consists of highly localized electric discharges occurring in the insulation serve as early indicators of such insulation degradation. Our research is focussed on using FBG sensors to detect the acoustic waves emitted from PD. Acoustic signals are captured in an in-house developed optical receiver using the tunable laser based interrogation technique. We plan to classify PD exploiting the fact that each type of PD exhibits a specific acoustic spectral signature. We have also carried out investigations on Cross Recurrence Plot Analysis based technique for localizing discharges.

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Reference :

K. Srijith, R. Sarathi, B.Srinivasan, Locating Partial Discharges in Power Transformers using Fiber Bragg Gratings, Australian Conference on Optical Fiber Technology (2015).

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Detecting Onset of Combustion Instability in Gas Turbines Through Fiber Optic Sensors

Combustion instability arises due to the coupling between unsteady heat release and acoustics of the chamber. Due to this coupling, high amplitude oscillations occur at the resonant acoustic mode of the combustor and . These oscillations are often self-sustaining and damaging to the system. In addition to causing extreme annoyance, combustion instability can result in hardware damage and can reduce the efficiency of the combustion system. Several approaches have been proposed to address this issue by finding precursors which can sense features that help to detect and characterize combustion instability While most of the existing methods utilize a single sensor to predict instability, most robust approach so far uses data from heterogeneous (e.g. pressure and chemi-luminescence) sensors and models spatio-temporal co-dependence among time series from the sensors to generate a data-driven precursor which is uniformly applicable across multiple experiment protocols with various premixing levels.

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Along this line, we are proposing a method where the chemi-luminescence sensor may be replaced by fiber optic bundle and the pressure sensor can be replaced by the FBG which offers advantages like immunity to electromagnetic interference, small size and multiplexing capability. In short, we are planning to provide a complete fiber optic solution for predicting combustion instability. Ongoing work is mainly to overcome the challenge faced by sensors in high temperature environment of the combustor.

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Reference :

Suma H, Vikram R, Satyanarayanan R C, Balaji Srinivasan, Predicting Onset of Combustion Instability in Gas Turbines Using Fiber Optic Sensors,IEEE Sensors (2020).