Abstract: This disclosure relates to non-destructive estimation of coating layer thickness based on sweep frequency photo acoustic guided wave technique. Coating of a substrate/surface protects it from wear, corrosion and serves the cosmetic aspect, hence making coating technology is an essential part industrial process. The existing techniques for coating thickness determination are either destructive or requires a prior knowledge of the refractive index of the surface under investigation or use of sophisticated instrumentation, complicated procedure and harmful radiation during industrial deployment. The disclosure utilizes an intensity modulated Continuous Wave (CW) laser diode to excite a sample thus, making the technique a partially contact based method. Further a calibration curve is plotted by determining a frequency spectrum and resonance frequency. The calibration curve is used for estimation of a coating layer thickness.

Abstract: This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.

Abstract: The disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.

Abstract: Use of Swept Frequency Acoustic Interferometry (SFAI) is becoming ubiquitous in taking non-invasive measurements of fluid parameters like sound speed, sound attenuation and density of fluid. But measurement using SFAI is relatively slow as one needs to sweep a wide range of frequencies and for each probing frequency one needs to wait for settling time. Further, SFAI works well only on steady flow as sudden change in fluid flow destroys resonance condition, thereby making it unsuitable for flowing fluid. Present application provides method and system for faster assessment of sound speed in fluids using compressive sensing technique. The system first uses random samples in defined frequency scanning range of frequency sweep signal for generating pseudo analytic signal vector. The system then estimates pulse-echo view by applying compressive sensing technique over pseudo analytic signal vector.

Abstract: Use of ultrasonic guided waves for damage identification and localization is not new in Non-Destructive Testing/Evaluation. However, most of the time it is performed with high voltage pulse excitations that use several hundreds of volts in the form of a short burst, thus making it unsafe and unsustainable for defect localization in large structures. Present disclosure provides a method and a system for damage localization using low power ultrasonic guided waves. The system of the present disclosure uses a Vector network analyzer (VNA) sweep of a defined frequency range of low signal amplitude on a structure to form guided wave resonance spectra. Then, the system performs an Inverse Fast Fourier transform (IFFT) on the guided wave resonance spectra to obtain a time domain pulse propagation picture. Thereafter, the system uses a pulse echo based analysis technique based on time domain pulse propagation picture to locate damage position in the structure.

Abstract: This disclosure relates generally to a method and system for damage localization on surfaces made of composites and metals. State-of-the-art methods for ultrasonic guided wave-based damage localization provide a reasonable accuracy. However, accuracy of prediction based on minimum number of observations is not yet achieved. The disclosed method provides damage localization by capturing response to the ultrasonic tone burst transmitted by a plurality of active piezoelectric sensors. The disclosed method provides a modified RAPID algorithm that considers an attenuation of the ultrasonic guided waves and factors energy of transmitted and received signals while predicting damage location. The method provides iterative grid search reduction mechanism to predict damage on the surfaces made of composites and metals.

Abstract: Temperature measurement is an important part of many potential applications in the fields of metallurgy. Conventional temperature measurement methods do not provide accurate and precise average temperature of fluid inside an enclosed chamber. The present disclosure provides multi-sensory techniques for measuring average temperature of mixed fluid inside a chamber. The average temperature is measured based on acoustic interferometry technique on standing wave and inputs received from one or more sensors and radar. The present disclosure utilizes radar to compensate the effect of fumes, noise based on Doppler effect. Further, the inputs received from the one or more sensors are used to determine the concentration of one or more fluids present in the chamber. The method of proposed disclosure depends on the principle of dependence of temperature on sound speed in fluid. So, measurement of sound speed can be mapped to report average temperature of mixed fluid inside the chamber.

Abstract: This disclosure relates to non-destructive estimation of coating layer thickness based on sweep frequency photo acoustic guided wave technique. Coating of a substrate/surface protects it from wear, corrosion and serves the cosmetic aspect, hence making coating technology is an essential part industrial process. The existing techniques for coating thickness determination are either destructive or requires a prior knowledge of the refractive index of the surface under investigation or use of sophisticated instrumentation, complicated procedure and harmful radiation during industrial deployment. The disclosure utilizes an intensity modulated Continuous Wave (CW) laser diode to excite a sample thus, making the technique a partially contact based method. Further a calibration curve is plotted by determining a frequency spectrum and resonance frequency. The calibration curve is used for estimation of a coating layer thickness.

Abstract: The disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.

Abstract: This disclosure relates generally to a method and system for online handwritten signature verification providing a simpler low cost system. The method comprises extracting signature data for the subject from a sensor array for the predefined time window at regular predefined time instants. Further, differentiating the matrix row wise and column wise to generate a row difference matrix and a column difference matrix. Further, determining an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix. Further, determining a plurality of signature parameters based on the row difference matrix and the column difference matrix.

Abstract: One of the biggest challenges faced by oil and gas companies is to monitor such long pipelines for leak events and generate false leak event alarms during routine pipe maintenance. A data associated with a first sensing unit is processed to obtain an instant timing information (T0) of a leak event in a conduit at a test environment. A data associated with a second sensing unit is processed to obtain a transient signal associated with the leak event at a specific band. An accelerometer data is filtered to obtain a band passed filtered accelerometer signal (Accelbpf). The Accelbpf is truncated in a time domain from the T0 to a duration Td of the leak event to obtain a temporal template signal (Acceltemplate). A leak event of a real-time conduit is dynamically detected at a physical environment based on Acceltemplate when a cross-correlation value is greater than a threshold value (?).

Abstract: Temperature measurement is an important part of many potential applications in process industry. Conventional temperature measurement methods require manual intervention for process monitoring and fail to provide accurate and precise measurement of temperature of an enclosed mixed fluid chamber. The present disclosure provides artificial intelligence based temperature measurement in mixed fluid chamber. A plurality of inputs pertaining to the mixed fluid chamber are received to build a fluid based model. The fluid based model is used to generate one or more fluid parameters. The one or more fluid parameters are used along with a ground truth temperature data and the received plurality of inputs for training an artificial intelligence (AI) based model. However, the AI based model is trained with and without knowledge of fluid flow. The trained AI based model is further used to accurately estimate temperature of the mixed fluid chamber for a plurality of test input data.

Abstract: This disclosure relates to a system and method for measuring average temperature of mixed fluid in an enclosed chamber. Measurement is based on two independent principle as measuring variation in acoustic wave velocity and variation in resistivity that works on a single setup. First principle is based on measuring acoustic wave velocity in a known medium, which is isolated from the surrounding. The system comprises a primary pipe and a secondary pipe, wherein the ends of the pipes reside inside the enclosed chamber. The primary pipe is made out of good conductor of heat and filled with air. Ends of the primary pipe is fitted with a transducers have one transmitter at one end and one receiver at another end. Average temperature of the mixed fluid is measured based on the variations in sound velocity of acoustic wave passed through the primary pipe and resistivity variations of the primary pipe.

Abstract: This disclosure relates generally to a method and system for determining viscosity information of fluids. The present disclosure utilizes an intensity modulated continuous wave (CW) laser diode-based PA sensing method to obtain a continuous wave photoacoustic (CWPA) spectra. Through this CWPA spectra, a full width half maximum (FWHM) and a spectral area is determined to obtain the information about the viscosity of fluids. Although, the CWPA based sensing technique is used for distinguishing different types of abnormalities in tissues, so far it is not used for measuring viscosity which is an important thermo-physical property. The viscosity information of the fluids from the normalized Gaussian fitted CWPA spectra is based on a viscosity feature computed from a FWHM, and a spectral area. The viscosity feature improves the good of fit parameter (R2) significantly to 0.98 as compared to the traditional only FWHM based viscosity determination for which R2 is 0.91.

Abstract: Fluids are normally transported from one place to another through pipelines. It is essential to monitor the pipeline to avoid leakage or theft. It is expensive and not feasible to install cameras and sensors along the whole length of the pipeline. A system and method for inspecting and detecting fluid leakage in a pipeline has been provided. The system is using vibration sensors along with pressure sensors to detect the leakage or theft along with the exact location of the leakage or theft. The pressure sensors are mounted on the pipeline so that the fluid touches the diaphragm of the pressure sensors to sense the wave generated due to leakage. The vibration sensors are mounted on top of the pipeline surface and on the nearby ground to eliminate general noise conditions. Moreover, two pressure sensors are also installed at opposite sides to pinpoint the leakage location.

Abstract: Embodiments herein provide a method and system for a non-invasive mechanism providing simultaneous determination of viscosity-temperature variation of a lubricant for predicting machine health using a Photo Acoustic (PA) sensing mechanism, Laser-enabled swept frequency acoustic interferometry (LE-SFAI), wherein the lubricant produces acoustic wave only if it absorbs the laser irradiation, thus overcomes the limitation of ultrasound based SFAI through optical absorption based contrast and proper selection of laser excitation wavelength. A PA signal received from the lubricant is processed by a Vector Network Analyzer (VNA), then converted to time domain to obtain normalized first peak that corresponds to the PA signal generated by the lubricant. A squared rise time of the first peak is indicative of viscosity of the liquid and shift in the first peak is indicative of variation of the viscosity as temperature of the lubricant varies.

Abstract: Use of Swept Frequency Acoustic Interferometry (SFAI) is becoming ubiquitous in taking non-invasive measurements of fluid parameters like sound speed, sound attenuation and density of fluid. But measurement using SFAI is relatively slow as one needs to sweep a wide range of frequencies and for each probing frequency one needs to wait for settling time. Further, SFAI works well only on steady flow as sudden change in fluid flow destroys resonance condition, thereby making it unsuitable for flowing fluid. Present application provides method and system for faster assessment of sound speed in fluids using compressive sensing technique. The system first uses random samples in defined frequency scanning range of frequency sweep signal for generating pseudo analytic signal vector. The system then estimates pulse-echo view by applying compressive sensing technique over pseudo analytic signal vector.

Abstract: This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.

Abstract: This disclosure relates generally to methods and systems for real time unobtrusive monitoring of physiological signals of a subject confined to a bed. Output of single channel continuous wave (CW) radars are dependent upon distance of the subject from the radar. Dual channel IQ radars are more accurate but are costly and availability in the market is a constraint. The present disclosure provides a cheap and easily replicable pseudo IQ radar based system. The pseudo IQ radar comprises two CW radars placed at a calibrated distance from each other such that optimum points of one CW radar spatially overlaps null points of the other CW radar and phase imbalance is suppressed. Three such pseudo IQ radars are positioned in a predetermined configuration around the subject being monitored. A Supervised Complex Signal Demodulation (SCSD) method configured to suppress amplitude and DC imbalance is also provided for evaluating the physiological signals.

Abstract: This disclosure relates generally to a method and system for determining viscosity information of fluids. The present disclosure utilizes an intensity modulated continuous wave (CW) laser diode-based PA sensing method to obtain a continuous wave photoacoustic (CWPA) spectra. Through this CWPA spectra, a full width half maximum (FWHM) and a spectral area is determined to obtain the information about the viscosity of fluids. Although, the CWPA based sensing technique is used for distinguishing different types of abnormalities in tissues, so far it is not used for measuring viscosity which is an important thermo-physical property. The viscosity information of the fluids from the normalized Gaussian fitted CWPA spectra is based on a viscosity feature computed from a FWHM, and a spectral area. The viscosity feature improves the good of fit parameter (R2) significantly to 0.98 as compared to the traditional only FWHM based viscosity determination for which R2 is 0.91.