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There has been a rise in interest in FOSs (fiber optic sensor) from scientists and engineers in several domains during the last few decades. It is precise, resilient, resistant to electromagnetic intervention, requires comparatively less power, and has a relatively low lifespan cost. These benefits make them superior to other common electrical sensors like piezoelectric sensors and strain gauges.  Sensors for tension, heat, acoustics, magnetic fields, hastening, rotary motion, pressure, moisture, and thickness have been created thanks to advances in our knowledge of the physics of light waves and their interfaces with their environment. Oil, energy and gas, biomedicine, civil engineering, atmosphere, and transport are just a few of the many fields in which it finds use. There is a common thread throughout all FOSs, and that is the detection of variable-induced changes in the light's intensity, phase, frequency, and polarization. Sensitivity and resolution are two measures of FOS performance. The compassion of a sensor is defined as the ratio of its output fluctuation to that of the measured variable. A highly sensitive sensor is one in which even tiny changes in the variable being monitored will result in substantial shifts in the sensor's output. The resolution of a sensor is its capacity to distinguish subtle changes. To put it simply, it is the amount by which the output value varies due to changes in the assessed variable, and it is defined as being equal to the uncertainty of the output. That is to say, if the resolution number is too high, then it won't be able to reliably detect changes below that threshold [1]. Fiber optic sensors are gaining prominence in vehicles, notably cars, owing to their resistance to electromagnetic and radiofrequency interference, the confidentiality of their data transfer, and the ease with which they may be installed because of their small size. Optical fiber sensors offer a lot of potential in the automotive industry due to their accuracy, reliability, and flexibility, but it is already being used in a wide variety of ways [2]. Fiber optic sensors can convey a signal over long distances with high precision and accuracy, and their compact size, high resolution, and low noise make them a desirable option. It also integrates several probed sensors multiplexed via a single fiber and is resistant to electromagnetic and radio frequency interference. To some degree, these benefits make fiber optic sensors superior to more conventional techniques and technologies for detecting damage [3]. Optical fiber in fiber optic sensors can be as thin as human hair, with common sizes ranging from 76 microns (3 mils) to 124 microns (5 mils) in diameter. Alterations to the path of light via the optical fibers could be used as a proxy for a broad variety of effect sizes. Measurements of strain (both longitudinal and transverse), pressure, temperature, and corrosion are all useful in infrastructure applications.

Figure 1: Fibre Optic Sensors [4]

Key Features of Fibre Optics Sensor Technology

Fiber optic sensor technology's key qualities that make it attractive for usage in infrastructure applications include :

a.     Resistance to Electromagnetic Interference

b.     Miniature in scope

c.     Environmental Sturdiness

d.     Multiplexing a Large Number of Fiber Optic Sensors

e.     Future Low-Cost High-Performance Devices

f.      Potential for Multiple Uses

g.     Various Lengths of Sensor Gauges [5]

Applications of fiber optic sensors in the Automotive sector

Diesel fuels and gasoline, which are complex hydrocarbons, are oxidized (combusted) rapidly to power today's automobiles. The combustion process releases several different gases, including water vapor (H₂O), carbon dioxide (CO₂), carbon monoxide (CO), oxides of nitrogen (NO_x), and oxides of sulphur (SO₂). Smoke and other particulate particles are also produced during the combustion process [6].A wide variety of systems, high-precision inertial positioning, military and civilian applications, and industrial process control, can benefit from fiber-optic interferometric sensors. One of the first uses of the interferometer was in strain measurement since it is basically a very sensitive strain gauge. Incorporating a deformable acceleration-sensitive mandrel into the standard mass spring fabrication process yields very sensitive accelerometers. Consequently, this has implications in fields like seismology and vibration analysis. Electric fields could be discovered using piezoelectric fiber varnishes or elements in the same way that pressure and temperature can be measured with great precision by the use of fibre interferometers. Measurement of liquid flow rate sensors by fibre interferometry has also been established [7].

Figure 2: Various Application fields of Fibre Optic Sensor (a) Steel beam

 (b) Reinforced Concrete beam [8]

This strategy has been employed by a wide range of authors, who then presented their findings after doing a literature review.

Min et al., (2021) [10] summarizes the fundamental sensing principles of optical fibre sensors used in marine atmosphere and marine structure strength monitoring, and their numerous sensing applications including natural parameters, organic parameters, and basic health monitoring. Based on these benefits, the principle of operation was outlined, and divided into two groups: point-based sensors and dispersed sensors, demonstrating the viability of optical fiber sensing technology in maritime applications. Temperature, pressure, salinity, pH, and other indicators of physical, mental, and structural health play a major role in the underlying theory and practice. Although many different fibre optic sensing methods have been developed and are in various stages of research and development, commercial availability of such sensors is still low. Optical fibre distributed sensing technology has been shown to have promising applications in maritime geophysics in recent years, suggesting that employing optical fibre cord as a disseminated sensor to display earth subtleties in the marine is a viable and practical approach.

Liu et al., (2020) [11] demonstrates a complete classification approach, including signal processing and feature extraction, and shows a vehicle detection and classification system that makes use of distributed fiber-optic acoustic sensing (DAS). Real-time vehicle identification, categorization, and speed estimate are all possible with this Rayleigh scattering light-based sensor system. With DAS technology, acoustic waves from any arbitrary location can be recognized and localized, with data being disseminated over the whole fiber connection. Sensing fibres in the structure of dispersed sensors capture traffic vibration signals and then have many attributes extracted from the signals in order to assess the number of vehicles and recognize vehicle classifications. As shown in the experiments, the upgraded wavelet denoising and dual-threshold algorithms work well for vehicle counting and speed evaluation, while the SVM algorithm achieves an accuracy of over 70% for classifying vehicles.

Young et al., (2020) [12] discuss the reliability of an automobile using a hybrid material junction, a unique approach of high-description fiber-optical sensing based on Rayleigh backscatter signal was presented to monitor the strain development in real-time. To reveal the stress and deformation of adhesive joints among carbon fibre synthesis and aluminium panes attached using fundamental epoxy-based adhesives, the performance of the "SmartJoint" was acquired through a simulated electrophoretic paint process with high spatial resolution. These studies suggest how this method might be used for on-demand sensing, giving a high density of measurements over a long distance with a gauge length of 0.65 mm. Insights gained from such measurements could be used to foresee the effects of time, temperature, and external mechanical stress on localized stresses along an adhesively attached component. In an irregular carbon fiber-reinforced composite with different fibre preferences and resin content in different areas, the fiber-optic sensor was capable to detect the spatial fluctuation of remaining stresses.

Mohankumar et al., (2019) [13] analyses in depth the recent innovations of self-propelled sensors for different automotive applications. Besides the standard temperature and pressure sensors, this study delves into the ins and outs of micromachined automobile sensors including pressure, location, velocity, angular rate, revolving speed, mass air flow, inertial, force, and thickness. Future autonomous vehicle applications are discussed, as is the utilization of nanoparticles and nanowire in the creation of automotive sensors. Regulation, protection, comfort, convenience, strength, and best-in-class driving are the main drivers of sensor use in the car industry. Sensors are crucial components of the powertrain, chassis, and comfort and convenience systems. Since consumers place a higher value on security, comfort, and convenience in their vehicles, the demand for sensors in this industry has a promising future.

Kuznetsov et al., (2019) [14] discusses the findings to solve a technological and scientific issue related to the early stages of the study and development of fiber-optic measuring systems for comprehensive monitoring of the operating condition of a traction motor, the humidity, wear, and motion of a brush-collector unit can all be measured thanks to special Bragg structures. After developing and validating mathematical models of the steps required to get these readings, a method was given for building devices to query such sensors.

Cong Du et al., (2019) [15] provides an overview of the recent state of the art uses of different fibre optic sensing (FOS) systems for observing railway structures’ operation (train speed and components) and structural health (rails, sleepers, stabilizer, bridges, subways, rail security, etc.). As a result of FOS's capacity to assess substantial quantities (such as stresses, temperature, displacements, fractures, etc.) uniformly all over the whole fiber's length, it is finding widespread use. For instantaneous monitoring that might aid in delivering early threat signals connected to timely identification of damages, these sensors can be efficiently incorporated into these structures because to their shape and adaptability. As a consequence of this quality, it has become a mature technology for prototyping, and its use in the railway sector is in great demand. Over more traditional approaches, it has showed a lot of promise. Engineers and academics with an interest in or commitment to the topic of railway infrastructure monitoring can benefit from a variety of monitoring applications.

Nedoma et al., (2019) [16] describes an alternative to conventional seismic stations that makes use of an interferometric sensor based on the Mach-Zehnder interferometer. The given sensor uses single-mode telecommunication optical fibres and a radiation source producing power in the milliwatt range to function in accordance with standard G.652.D. All cars were identified by the fiber-optic sensor, and the data was compared to that from a typical seismic station. The findings suggest that this sort of sensor might replace more expensive and bulky traditional sensors in the monitoring of certain rail transit characteristics. Electromagnetic interferences from modern traction motors in rail cars and signal contamination from return traction current in rails are the key reasons for limiting the usage of traditional sensors. As shown in the experiments, the upgraded wavelet denoising and dual-threshold algorithms work well for vehicle counting and speed estimation, while the SVM algorithm achieves an accuracy of over 70% for classifying vehicles.   

A.Iele et al., (2018) [17] demonstrated the use of fibre optic sensors for in-situ, isolated, and real-time monitoring of the weight applied to aircraft landing gear, and the results of this investigation were given. Various Detection Mechanisms Fiber Bragg Gratin (FBG) sensor networks enclosed in a plastic housing have been successfully developed, manufactured, and installed on authentic Landing Gears (LGs). In particular, a twin-engine, 10-seat, 4.8-ton helicopter with a number of FBG strain sensors installed on its Main and Nose landing gears served as a proof-of-concept for the proposed system, , and a sizable experimental effort was undertaken to ensure the integrity of the circuits of optical strain sensors and the viability of using the information provided by the optical system to accurately regulate in real-time, the strain on each landing gear. Better precision, greater stability, reduced size, and protection from electromagnetic interference are just some of the benefits of using FBG sensors instead of more traditional methods. Optical sensing's immunity to lightning and resilience to metallic corrosion make it a promising technology for use in open air and severe situations, where it has the potential to lower long-term maintenance costs.                        

Fabian et al., (2018) [18] discusses an all-optical sensing system that was made to fit snugly within an electronic motor, and which leverages the ocular kind of measure and the insulate kind of the sensor substance to prevent electrical intervention. The Fiber Bragg Grating (FBG)-based method used has allowed for the synchronized supervising of key constraints involving machine tremor, blade speed, force, spinning way, heat circulation along the stator coil and on the rotor surface as well as the stator coil wave rate. All perception devices are embedded into the device and controlled by a central sensing debriefing unit, minimizing the number of external parts typically used in sensor systems while also simplifying the user interface. The perception system has been effectively incorporated into and analyzed on a durable electromagnet motor pattern, and the design of the system and the results of its testing and assessment are provided.

The below Table 1. describes the summary of the Review of Literature and the method used in their studies by the authors. 

Table 1: Summarize the review of the literature

Author	Methods	Outcomes
Min et al., (2021) [10]	FOS	Using both point-based sensors and distributed sensors, this demonstrates the potential of optical fibre sensing technologies for use in maritime settings.
Liu et al., (2020) [11]	SVM and  Fibre optic (Distributed Acoustic sensing)DAS	This enhanced wavelet denoising and dual-threshold algorithms count and estimate vehicle speeds effectively.
Young et al., (2020) [12]	FOS-based Rayleigh backscatter signal	High-definition fiber-optic sensors can monitor vehicle strain using the Rayleigh backscatter signal.
Mohankumar et al., (2019) [13]	Micromachined and Micro acoustic automotive sensors	Pressure, location, velocity, angular rate, rotating speed, mass air flow, inertial, force, and thickness sensors are explored.
Kuznetsov et al., (2019) [14]	FOS and FBG	scientific and technological outcomes of developing fiber-optic measurement systems for integrated monitoring.
Cong Du et al., (2019) [15]	FOS	The sensors' design and flexibility allow real-time monitoring and early damage identification.
Nedoma et al., (2019) [16]	FOS	The findings indicate this sensor might replace costly and cumbersome standard sensors in rail transit monitoring.
A.Iele et al., (2018) [17]	FBG sensors and FOS	Study examining the viability of utilizing fiber optic sensors for in situ, remote, real-time landing gear load monitoring.
Fabian et al., (2018) [18]	FOS and FBG	A prototype permanent magnet motor has had its sensing system successfully integrated into and tested.

1. Comparison  Analysis

The authors examine the comparative analysis of the models utilized by different writers in this part. Classification methods such as FOS, SVM, FODAS, and FBG were used to test the suggested technique. FBG and FOS when worked together have a higher accuracy of 98 % than FOS, SVM+ FODAS, FOS+FBG, FOS, FBG+FOS, FOS+ FBG which have an accuracy of 78%, 70%, 94%, 90%, and 98%, 94% respectively. The accuracy of a model can be measured by how many instances it correctly predicts. Using the equation 1 in the study, the construction of a problem involving a binary classification of two classes. Accuracy of the following methods is measured by the percentage of correct diagnoses (denoted by the abbreviations TP, TN, FP, and FN): True positive, True negative, False positive, and False negative respectively.

Comparative studies of several models are listed in Table 2.

Table 2: The accuracy of numerous techniques

Authors	Methods	Accuracy
Min et al., (2021) [10]	FOS	78 %
Liu et al., (2020) [11]	SVM and FODAS	70 %
Kuznetsov et al., (2019) [14]	FOS and FBG	94 %
Cong Du et al., (2019) [15]	FOS	90 %
A.Iele et al., (2018) [17]	FBG sensors and FOS	98 %
Fabian et al., (2018) [18]	FOS and FBG	94 %

Figure 3. comprises the comparison graph of various techniques used by the authors in this paper

Figure 3: Comparison graph of Accuracy

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