A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter

Accepted Manuscript A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter Krishnendu Chatterjee, Deboshree Roy, Suneet Tuli PII: DOI: Reference:

S1350-4495(16)30408-X http://dx.doi.org/10.1016/j.infrared.2017.02.015 INFPHY 2246

To appear in:

Infrared Physics & Technology

Received Date: Revised Date: Accepted Date:

9 August 2016 24 February 2017 25 February 2017

Please cite this article as: K. Chatterjee, D. Roy, S. Tuli, A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter, Infrared Physics & Technology (2017), doi: http://dx.doi.org/ 10.1016/j.infrared.2017.02.015

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A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter Krishnendu Chatterjee, ∗ Deboshree Roy and Suneet Tuli † , Centre for Applied Research in Electronics IIT Delhi, India ∗ Corresponding author e-mail id- deboshree roy22yahoo.com

Abstract—This paper proposes a novel pulse compression algorithm, in the context of frequency modulated thermal wave imaging. The compression filter is derived from a predefined reference pixel in a recorded video, which contains direct measurement of the excitation signal alongside the thermal image of a test piece. The filter causes all the phases of the constituent frequencies to be adjusted to nearly zero value, so that on reconstruction a pulse is obtained. Further, due to band-limited nature of the excitation, signal-to-noise ratio is improved by suppressing out-of-band noise. The result is similar to that of a pulsed thermography experiment, although the peak power is drastically reduced. The algorithm is successfully demonstrated on mild steel and carbon fibre reference samples. Objective comparisons of the proposed pulse compression algorithm with the existing techniques are presented. Index Terms—Thermal imaging NDT, frequency modulated thermal wave imaging, pulse thermography, Pulse compression, band-pass filter.

I. INTRODUCTION

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HERMAL IMAGING non destructive technique (NDT) is a relatively new technique for defect detection. The presence of defects are indicated by a change in surface temperature of the defective region, when the sample is heated through different excitation technique. Common excitation methods are pulsed excitation, step excitation and continous wave excitation. Pulse thermography [1],[2] is a technique, where a high intensity source heats the sample for a brief duration. The defective region has a lower cooling rate,that is recorded with an infra-red camera . In practice, this technique requires a high power excitation source. In lock-in thermography [3]-[4] the excitation source varies periodically with time. This generates a periodic variation of temperature on the surface, known as thermal waves. The penetration of thermal waves in the sample is dependent on the excitation frequency and thermal properties of the sample and is known as diffusion length [5]. The propagation of thermal waves generates a change in phase and amplitude at different points of the sample. † Prof. Suneet Tuli is currently Dean Engineering and Applied Science at Bennett University, Greater Noida, India

This property is used to generate phase and amplitude images. Frequency modulated thermography [6]-[8] is a technique similar to lock-in thermography with a limited band of frequency as an excitation technique. The technique is advantageous as it generates a range of phase images simultaneously, and saves the time of conducting multiple lock-in experiment at different frequencies. Lately, an extension of frequency modulated excitation technique has been explored [9]-[10]. The chirped response of the sample is correlated with a reference signal to generate a response of a pulsed thermography output. The output video is known as pulse compressed video. The reference signal is specifically important in processing part, as they are used to generate a compression filter, that acts individually on all pixels of recorded video to generate a compressed video. The published work in the area [11]-[12] considers any arbitrary non-defective point on the sample as a reference signal. Any deviation in reference signal affects the complete compressed video. In this paper an objective comparison between the existing pulse compression technique and the proposed technique is presented. An additional band-pass filter in the proposed compression algorithm reduces the out of band temporal noise.

II. E XISTING P ULSE C OMPRESSION T ECHNIQUES Pulse compression is a signal processing technique prevalent in RADAR for target detection [13]-[15]. Therein, a pulsed signal is dispersed to form a linear frequency modulated signal (a chirp), which is transmitted. The time delayed signal reflected from the target is finally correlated with a reference signal, to obtain a pulsed response. Pulse compression in the context of frequency modulated thermal video is also based on aforesaid concept.

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sample described in Fig. 1, and an infra-red camera for recording the sample thermal response. The FLIR silver SC5000M cooled mid-wave infra-red camera is 14-bit with 320×240 resolution. The excitation signal in FMTWI comprises of a linear up-chirp whose bandwidth spans over the frequencies necessary to produce thermal diffusion lengths to cover the range of defect depths to be detected. The bandwidth used in this case is, 0.01-0.1 Hz for a duration of 900 sec at a sampling rate of 10 samples per second. The corresponding CFRP diffusion length for the given chirp bandwidth is depicted in table (I). The timebandwidth product is another important parameter in shaping of the resultant pulse in pulse compression technique. The time-bandwidth product should be as high as possible to generate an output of a pulsed excitation. In present case the time-bandwidth product is 81. TABLE I: Thermal Diffusion Length for CFRP.The parameter values ([8]) used are- k|| = 4 W/mo C; ρ= 1600 kg/m3 ; c= 1200 J/kg o C Frequency (Hz)

Fig. 3: Cross-correlation pulse compression image. The pixel in red indicates the reference point.

The experimental source consists of a 40 W LED for excitation purpose along with its modulation circuitry, a carbon fibre reinforced polymer(CFRP)

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The camera recorded video of the test-piece is pixelwise auto-correlated to obtain a resultant compressed pulse video output. Fig. 2 shows a best image from the output video. However, in this technique, a delay

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in defect response time with respect to background is essential. Hence, images may improve, when the raw video is cross-correlated with a selected background. This selected background point is a reference signal for pulse compression video. An image is shown in Fig. 3 from the cross-correlated compressed pulse video output with a red point in image indicating reference signal. The delay in occurence of compressed pulse peak time with respect to reference point quantifies a defect depth. Hence a careful choice of reference point is essential for proper defect depth quantification. For accurate results, excitation source signal as a reference signal is considered over any arbitrary non defective region. An algorithm is proposed that considers excitation source as a reference signal.

III. P ROPOSED P ULSE C OMPRESSION T ECHNIQUE A. Theory Pulse compression so far is dealt in time domain. In frequency domain, chirp and a pulse have an identical amplitude spectral component, in a band of frequency. However, the difference in the two signals lies in their phase components. For a pulsed signal, all the frequency components are in the same phase. Hence, they interfere constructively to generate maximum amplitude at a given time. On the contrary, in a chirped signal, the phases for different frequency components are such that, the signal is dispersed in time domain. The signal is reconverted to a pulsed form by removing the dispersion. The dispersion is removed by using a filter derived from a reference signal, whose phase spectrum matches with the original chirped signal.

B. Experimental set-up for pulse compression algorithm The basic experimental set-up consists of an excitation source along with a test piece and an infra-red camera. An additional circuitry to record the reference signal is required, that is the modulated excitation source. The inherent resistivity of the excitation source may prevent its optical output to follow the original electrical signal. The actual optical response in such a case is the true reference signal for pulse compression. This reference signal when processed with a set of algorithms generates a pulse compression filter. A compressed pulse is generated by applying the compression filter on the recorded thermal response.

C. Algorithm Fig. 4 shows the complete pulse compression algorithm used in this work.

1) The chirped excitation signal is incident on a sample. The thermal response of the sample is recorded with an infra-red camera. All information regarding the defect profile is present in this response. The excitation signal is simultaneously recorded and forms the reference signal for pulse compression. 2) The reference signal contains a frequency component at 0 Hz. This is removed by polynomial fitting. The process is known as offset removal. 3) The offset removed signal is then fourier transformed to obtain an amplitude and phase spectrum. 4) Compression filter - the fourier transformed signal obtained in previous step is processed with a sequence of steps to obtain a) The phase spectrum of the signal is shifted by 1800 b) The resultant signal is passed through a band-pass filter to suppress unwanted noise component outside the original chirped band of frequencies. c) The amplitude spectra is normalized to one. This standardizes the excitation level during the processing of the pulse compression algorithm. 5) The recorded thermal response (i.e. temperaturetime variation) of the chirp excited sample for a given pixel is considered and offset subtracted. Each pixel of the video are then individually fourier transformed. 6) The fourier transformed thermal response obtained in the previous step is processed with the designed pulse compression filter. The product is inverse fourier transformed (IFFT) to obtain the output of a compressed pulse. The algorithm is applied individually on all pixels of the chirp excited sample video to obtain pulse compressed video. The compressed video is expected to resemble that of a pulse thermography experiment, with a considerable reduction in the excitation source peak-power. An objective comparison of existing techniques in Fig. 2 and 3 with the proposed technique is depicted with a CFRP sample in section IV. The algorithm is further applied in a mild steel sample in section V. Unilateral thermal conductivity, and a homogeneous medium allows a simpler heat flow analysis for a mild steel sample.

IV. A PPLICATION ON CFRP SAMPLE The pulse compression experiment is carried out on CFRP (Carbon Fibre Reinforced Polymer) samples. The primary experimental set-up is the same as in section II, with an additional circuitry to record the reference signal. The block diagram of CFRP sample

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is depicted in Fig. 1. A 40 W LED is used as an excitation source, a mid-wave FLIR infra-red camera, a CFRP test piece ,a LDR for recording the excitation source, and a control circuitry that synchronizes these independent system with a master clock . The LED modulation circuitry generates only square waves to reduce the complexity in circuit design. The excitation source is simultaneously sensed with light detecting resistor (LDR), digitized with electrical comparator

in schmitt trigger configuration, and recorded with a micro-controller (Arduino UnoR3) in control circuitry. The microcontroller simultaneously sends modulation signal to the LED driving circuitry, and trigger signal to camera for frame capture. The block diagram of experimental set-up is shown in Fig. 5 The excitation signal is an up-chirp with frequency varying from 0.01 to 0.1 Hz for 900 sec and acquired

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at 10 samples per second. The recorded data is processed with an algorithm described in section III-A. Fig. 6 shows the reference signal recorded by LDR, and thermal response signal of the test-piece at single pixel, and the resultant compressed pulse. The resultant pulse compressed images is depicted in Fig. 7b. The Fig. 7 provides a comparison of proposed

pulse compression algorithm with the existing pulse compression cross-correlation technique. The figure shows that the clarity of the proposed technique is better specifically for smaller defects with 4 mm diameter.

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(a) Cross-correlation pulse compression image. The pixel in red indicates the reference point.

respectively. The contrast curves resembles that of a pulsed thermography experiments. For deeper defect the contrast curve maximizes later, and with much lesser amplitude. This is in agreement of pulse thermography where the deeper defects are known to appear later.

SNR Calculation

(b) Image from proposed pulse compression algorithm described in section III-C Fig. 7: Comparison of proposed pulse compression algorithm with the existing pulse compression cross-correlation technique

Contrast Curve The defect detection is quantified with the variation of defect signal contrast with frames. The contrast for a given defect is defined as the difference of the defect signal with its local background. The contrast curve depicted in Fig. 8 is obtained by following sequence of steps1) The first step in contrast curve determination is to remove the surface non-uniformity that arises due to spot heating or non-uniform heating of the excitation source. The spatial fitting is carried on each frame image at a time. The thermal image is considered as a 2-D surface, where the z axis represents the temperature. The surface is fitted. 2) The standard deviation between fitted data and image surface is calculated. The data pixel greater than the calculated deviation are excluded as they represent signal (of the defect), and the remaining pixels are put to zero. 3) Each defect location is identified, and a gaussian fitting is carried out to generate a signal amplitude for a given defect. Steps from (1)-(3) are individually applied on all frames of the compressed video to obtain contrast curve for a defect. Fig. 8a and 8b represent defect contrast curve for defect diameter 4 mm and 6 mm

The proposed pulse compression algorithm is compared with the existing techniques through SNR.The SNR is calculated by the following method- The peak in the contrast curve described in Fig. 8 is used as signal for SNR calculation. The noise is defined as the root mean square of the non-uniformity removed surface described in step (2) of contrast curve calculation. The variation of SNR with defect depth is shown in Fig. 9 for two set of algorithms-the cross correlation algorithm and proposed pulse compression algorithm. The defects considered for SNR calculation are depicted in F-F Section in Fig. 1. Fig. 9 shows that the proposed algorithm has a higher SNR when compared with the commonly used cross-correlation algorithm. The proposed pulse compression algorithm is specifically useful for deeper defect detection as the SNR reduces significantly for cross-correlation algorithm. V. A PPLICATION ON M ILD S TEEL S AMPLE The experimental set-up consists of a test piece depicted in Fig. 10, an excitation source set-up, an infrared camera for recording the thermal response, and a set-up for recording reference signal. The excitation source consists of two 1 kW halogen lamps each that generates a chirp heating using a signal generator and amplifier. The experimental setup is shown in Fig. 11. The Indigo Merlin is an electrically cooled InSb focal plane array(FPA) camera with a 12-bit digital output and a resolution of 320×256 (width×height). The camera has a maximum framerate of 60 Hz and an NETD of less than 25 mK. IR screens, consisting of glass tanks containing water (30 mm of water and 5 mm glass on each side), were used to remove the IR radiation emitted by

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the halogen lamps that may have interfered with experiments. Also a smaller 500 Watt reference lamp was placed alongside the setup which is seen through two glass reectors to cut down its intensity. The experiment is carried out with excitation frequency varying from 0.0 to 1 Hz for a duration of 100 sec with a sampling rate of 10 samples per second. The thermal properties of mild steel are listed in table II. For a given chirped excitation signal, the diffusion length varies from infinity to 2.05 mm for

an excitation frequency of 0 - 1 Hz. TABLE II: Thermal properties of mild steel Material property Thermal conductivity Density Specific Heat

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The recorded video is processed by an algorithm described in section III-C. The steps in pulse

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Fig. 14: Resultant pulse compression image

SNR Calculation The signal to noise ratio (SNR) for a given defect is calculated by a sequence of steps1) The non-uniformity in heating of the surface image is removed by a 3-D surface fitting. The fitted surface is subtracted from the image, to obtain a flattened surface. The 3-D flattened algorithm is applied to all the frames of the output compressed pulse video individually. 2) For a given defect, a number of pixels are selected manually within the defect region, that represents the defect. The average of these pixels denote the defect amplitude, and the maximum defect amplitude for a given frame is considered as a signal. 3) The root mean square of the flattened surface is considered as a noise. VI. C ONCLUSION A new pulse compression algorithm is proposed and is experimentally compared with the existing

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compression are shown in Fig. 12 and 13. The glass reflection of 500 W reference lamp is placed alongside the test sample during recording. In Fig. 12, the reference signal and the sample placed together is shown in an image captured by the camera. Fig. 12a shows the time variation of reference signal, and its offset subtracted signal. The amplitude and phase spectrum of offset removed reference signal is shown in Fig. 12c. The signal is phase shifted by 1800 and passed through a band-passed filter to remove the frequency component above 1 Hz. The amplitude spectrum is further normalized to one. Fig. 12d depicts that the processed phase and amplitude spectrum. The information is now present in phase spectrum only. Fig. 12b shows thermal response of mild steel sample to chirped excitation. The amplitude and phase spectrum of the temperature response is displayed in Fig. 12e. Signals in Fig. 12e and Fig. 12d are multiplied and inverse fourier transformed to obtain the resultant compressed pulse (Fig. 13g). The algorithm is applied on all pixels of the sample image to obtain a pulse compressed video. A frame in the compressed video (Fig. 14) with all defects visible is obtained.

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pulse compression techniques. Initial comparison of auto-correlated and a reference signal based crosscorrelated pulse compression is explored. It shows the judicious selection of reference signal is necessary for defect detection in this technique. The proposed pulse compression algorithm derives the reference signal from excitation source itself which is further processed to generate a compression filter for pulse compression algorithm. A band-pass filter in algorithm removes noise outside chirped excitation frequency range. An objective comparison of the proposed algorithm with the existing pulse compression technique on a CFRP sample shows better result specifically for smaller defects. The SNR calculation shows the efficiency of proposed technique over the preexisting cross-correlation technique for deeper defects. Two different techniques of acquiring the reference signal is explored with CFRP and mild steel samples. The results are further quantified for subsurface defect detection. Fig. 8 and 15 resembles pulsed thermography contrast and SNR curves respectively,and can be concluded that the output of compressed pulse video resembles that of a pulsed thermography video.

R EFERENCES [1] N.P. Avdelidis, D.P. Almond, A. Dobbinson, B.C. Hawtin, C. Ibarra-Castanedo, X. Maldague, “Aircraft composites assessment by means of transient thermal NDT, Progress in Aerospace Sciences”, Volume 40, Issue 3, April 2004, pp. 143162. [2] Lau, S. K., D. P. Almond, and J. M. Milne. “A quantitative analysis of pulsed video thermography ”, NDT & E International Vol.24, Issue 4, 1991, pp 195-202. [3] G. Busse, D. Wu, and W. Karpen, “Thermal wave imaging with phase sensitive modulated thermography”, Journal of Applied Physics Vol. 71, Issue 8, 1992, Jan. 1992, Pages 3962-3965 [4] D. Roy, K. Chatterjee and S. Tuli, ”Characterization and energy absorption efficiency determination of LED as an effective photothermal excitation source in lock-in thermography,” in IEEE Sensors Journal, vol. 15, no. 10, pp. 6010-6016, Oct. 2015. [5] K. Chatterjee, S. Tuli, “Image Enhancement in Transient LockIn Thermography Through Time Series Reconstruction and Spatial Slope Correction”, Instrumentation and Measurement, IEEE Transactions on, Vol. 61, Issue 4, Apr. 2012, Pages 10791089./

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[6] Mulaveesala, R. and S. Tuli, “Theory of frequency modulated thermal wave imaging for nondestructive sub-surface defect detection,” Applied Physics Letters, Vol. 89, No. 19, 2006. [7] Mulaveesala, R., P. Pal, and S. Tuli, “Interface study of bonded wafers by digitized linear frequency modulated thermal wave imaging,” Sensors and Actuators A, Vol. 128, 2006, pp. 209216. [8] K. Chatterjee, S. Tuli, Simon G. Pickering, and Darryl P. Almond. “A comparison of the pulsed, lock-in and frequency modulated thermography nondestructive evaluation techniques”, NDT & E International Vol. 44, Issue 7, Nov. 2011, Pages 655-667. [9] S. Tuli, and R. Mulaveesala,“Defect detection by pulse compression in frequency modulated thermal wave imaging.” Quantitative InfraRed Thermography Journal Vol.2, Issue 1, Jun 2005, pp. 41-54. [10] R. Mulaveesala, and Subbarao Venkata Ghali. “Coded excitation for infrared non-destructive testing of carbon fiber reinforced plastics.” Review of Scientific Instruments Vol. 82, Issue 5, 2011 [11] V. Arora, and R. Mulaveesala. “Pulse compression with Gaussian weighted chirp modulated excitation for infrared thermal wave imaging.” Progress in Electromagnetics Research Letters Vol. 44, 2014, pp. 133-137. [12] R. Mulaveesala, J. S. Vaddi and P. Singh “Pulse compression approach to infrared nondestructive characterization”, Review of Scientific Instruments, Vol. 79, Issue 9, 2008. [13] Klauder, John R., A. C. Price, Sidney Darlington, and Walter J. Albersheim. “The theory and design of chirp radars”, Bell System Technical Journal Vol. 39, Issue 4, 1960, pp. 745-808. [14] C. E. Cook, “Pulse compression-key to more efficient radar transmission”, Proceedings of the IRE, Vol. 48, Issue 3, 1960, pp.310-316. [15] George Turin, “An introduction to matched filters”, IRE transactions on Information theory Vol. 3, Issue 6, 1960, pp 311329.

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Highlights     

A novel pulse compression algorithm for defect detection. A novel technique to acquire the reference signal used in the algorithm A band-limited filter in algorithm removes out of band noise Algorithm is implemented on CFRP and mild steel samples Output similar to pulse thermography at reduced peak-power.