The probability imaging algorithm of air transport pallet adhesive failure monitoring

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1 The probability imaging algorithm of air transport pallet adhesive failure monitoring More info about this article: Bin LIU, Fanqing MENG, Wenjuan WANG, Li an CHEN Department of Aviation Oil and Material, Air Force Logistics College, Xuzhou, China Corresponding author: Bin Liu, Abstract In order to improve the safety and economy of the air transport pallet, as for there is no method to monitor the adhesive failure. A damage probability imaging algorithm based air transport pallet adhesive failure monitoring method is proposed. For the innovate air transport pallet adhesive failure monitoring method, the damage index of each piezoelectric excitation-sensor channel is calculated by the envelope damage index calculating method of damage scattering signal firstly. Then, the structural damage probability image is obtained by the damage probability imaging algorithm. Finally, the air transport pallet adhesive failure position is calculated by the coordinate probability weighted algorithm. A test piece is made to verify this innovate method. The verification results show that the method proposed is able to locate the air transport pallet adhesive failure without the signal propagation velocity, and the error of localization is less than 2cm. Keywords: Air transport pallet, Adhesive failure monitoring, Damage index, Damage probability imaging, Coordinate probability weighted 1. Introduction With the continuous expansion of national interests, the role of strategic delivery continues to grow, and the task of air containerization is increasing rapidly. 2.5 tons and 4.5 tons of air transport container pallets as an important component of aviation integrated transport support equipment, which have been used a large number of transport units. In the air transport pallet using process, which is often rolled, fork loading and hoisting, deformation of complex structure, it is easy to cause the skin and core layer degumming, thereby compromising the integrity and mechanical properties of air transport pallet. Air transport pallet severely weakened the strength and deflection. The air transport pallet is a sealing structure, the human eye cannot discover the internal degumming, and it will seriously threaten the goods, aircraft and personnel safety handling. In addition, due to the inability to accurately grasp the structural state of air transport pallet inside now provides air transport pallet in use after three years to overhaul, which many of them have excellent performance, which caused great waste. Therefore, the urgent need to degumming research on monitoring technology of air transport pallet, grasps the structure of state air transport pallet inside, so as to improve the safety and economy of air transport pallet use. At present, the test method of air transport pallet tapping check, it uses a small hammer, a knock on the surface of each quadrant of 25mm 25mm, if the knocking sound is more boring, indicating the existence of defects between the skin and the core layer. The method is greatly influenced by human experience and cannot be found to be less degumming. Ultrasonic inspection is a mature non-destructive testing method at present. However, compared with a large number of frequently used 2743mm (long) 2235mm (wide) air transport pallets, this method is time-consuming and laborious. Therefore, Zhang should study the composition and application of American 463L pallet system [1]. Rui put forward the air transport pallet operation technology from two aspects of the overall process and key links, and from daily use, pad use, four aspects of information technology and put forward the maintenance tray of air transport pallet management method [2]. The current research cannot realize the effective monitoring of air transport container pallets. 1

2 Structural health monitoring technology for the prevention of major accidents, improve the safety of the structure, reduce economic losses, has important scientific significance and broad application prospects of reduces the maintenance cost, the protection of major construction projects [3-4]. In recent years, piezoelectric sensor networks and Lamb wave damage imaging and position estimation has been widely studied [5], such as delay-cumulative damage imaging [6-7], ultrasonic phased array imaging [8-9], time reversal imaging [1-12], multiple signal classification [13-14] and spatial-wavenumber filter imaging [15-17], but these methods need to obtain the prior velocity signal damage imaging. The air transport pallet from the skin and core layer and adhesive, and with the use of the change of state, the characteristics of the structure will change, resulting in anisotropy which highlights the signal, the method cannot achieve accurate monitoring of the degumming. The damage probability imaging method [18-22] use the damage index in each excitation sensing channel to an image, and does not depend on the wave velocity of the signal. It is suitable for complex composite structures. In this paper, the application of damage probability imaging method in the monitoring of air transport container pallets is explored, which provides a method for on-line structural health monitoring of air transport container trays. 2. Adhesive failure monitoring principle Use the damage factor incentive on the sensing channel damage probability imaging method, calculation of the influence of regional incentive-sensing channel damage index according to ellipse trajectory method, then the elliptic regional influences on each channel damage index synthesis imaging, finally using probability weighted algorithm to calculate the damage location coordinates [18-22]. In the use of damage probability imaging method, first of all, according to the structural health monitoring task requirements, M piezoelectric sensors are arranged on the monitored structure, thus forming N excitation sensing channels. 2.1 Damage index Air transport pallet degumming will transfer function of the structure caused by the change, thus affecting the amplitude and phase, so the use of sensor signals, the damage factor of damage scattering signals based on the envelope, as shown in Eq. (1): Di- j t -H i- j t dt DIi- j =, i j H t dt i- j (1) Where, DIi-j is the damage index of the channel which i piezoelectric sensor excitation and j piezoelectric sensor sensing. Di-j(t) is the on-line monitoring signal of piezoelectric sensor j that the excitation piezoelectric sensor is i. Hi-j(t) is the health reference signal of piezoelectric sensor j that the excitation piezoelectric sensor is i. i is the number of excitation piezoelectric sensor. j is the number of sensing piezoelectric sensor. t is the sampling time. 2.2 Damage Probability Imaging According to the task requirement of the structural health monitoring, the monitored area is divided into each one pixel, then, the damage probability of each pixel is: B R i j P Pi j DIi j[ ] (2) i j i j B 1 Where, Ri j, Ri j B R i j B, Ri j B (3) 2

3 R i j ( x x ) ( y y ) ( x x ) ( y y ) i i j j ( x x ) ( y y ) 2 2 j i j i (4) P(x, y) is the damage probability of the pixel (x, y); Pi-j(x, y) is the damage probability of the pixel (x, y) in the excitation-sensing channel of i piezoelectric sensor excitation and j piezoelectric sensor sensing. DIi-j is the damage index of the excitation-sensing channel of i piezoelectric sensor excitation and j piezoelectric sensor sensing. B is the dimension parameter which controls the influence range of the excitation-sensing channel damage index. Ri-j(x, y) is the distance ratio of between the (x, y) and i piezoelectric sensor and j piezoelectric sensor with the distance between the excitation-sensing channel. R i-j(x, y) is the distance ratio after comparison with the size parameter B. (xi, yi) is the coordinate of the i piezoelectric sensor. (xj, yj) is the coordinate of the j piezoelectric sensor. (x, y) is the coordinate of the pixel. The damage probabilities of all pixels in the monitoring region are calculated and normalizing them. Then, the structure damage probability image can be obtained by imaging the normalized damage probabilities, as shown in Fig. 1. Excitation sensor 1 Boundaries of influence and beyond,p ij = Excitation sensor 2 Direct path, P ij=di Damage 2.3 degumming location Fig. 1 Sketch map of damage probability imaging According to the structural damage probability image of the monitoring area, the position coordinates of the air transport container pallets are calculated by using the probability coordinates weighting algorithm of Eq. (5): L Ly x xh P( xh, yk ) h1 k 1 xd L L x y P( xh, yk ) h1 k 1 L L x y y j P( xh, yk ) (5) h1 k 1 yd L L x y P( xh, yk ) h1 k 1 Where, (xd, yd) is the coordinates of the adhesive failure. xh is the x axis coordinates of the (xh, yk) in the structural damage probability image. yk is the y axis coordinates of the (xh, yk) in the structural damage probability image. P(xh, yk) is the damage probability of the (xh, yk) in the structural damage probability image. Lx is the x axis length of the monitoring area; Ly is the y axis length of the monitoring area. 2.4 Implementation process Sensing sensor 2 Based on the previous three sections, the implementation process of the air transport container pallets monitoring based on damage probability imaging method is shown in Fig. 2. First of all, 3 Sensing sensor 1

4 M piezoelectric sensor is arranged in the monitored structure, N excitation-sensing channels are constituted. The health reference signals Hi-j(t) can be obtained by collect each excitationsensing channel Lamb wave signal in the structure health state. The online monitoring signals Di-j(t) are obtained after the air transport pallet adhesive failure. The damage index DIi-j of each excitation-sensing channel is calculated. The monitored area is divided into each one pixel (x, y) according to the task requirement of the structural health monitoring. The damage probability Pi-j(x, y) of each pixel in each excitation-sensing channel is calculated. The damage probability P(x, y) of the pixel can be obtained by composition the damage probability of each excitationsensing channel. The structural damage probability image can be obtained by normalized and imaging the damage probability of each pixel in the monitored region. The position coordinates (xd, yd) of the adhesive failure is calculated by the probabilistic coordinate weighting algorithm. The on-line monitoring signals (D i-j (t), ( i [1, N], j= [1, N],i j)) The health reference signals (H i-j (t), (i [1, N], j= [1, N],i j)) The monitored area is divided into each one pixel The damage index of each excitation-sensing channel (DI i-j ) Select a pixel point (x, y) Calculate The damage probability of the pixel in each excitation sensing channel (P i-j (x, y)) Superimpose the damage probability in each excitation sensing channel (P(x, y)) All pixels have been calculated NO Yes normalized and imaged the damage probability of all pixels in the monitored region (Structural damage probability image) Calculate the position coordinates of the adhesive failure by probabilistic coordinate weighting algorithm (x D, y D ) Fig. 2 The implementation process of the air transport container pallet degumming monitoring 3. Experimental verification 3.1 experimental equipment and process According to the implementation process of air transport pallet, a 1mm 1mm (length width) specimen is made. The upper of the specimen is 775 Aluminium Alloy plate, and the bottom is Bashar wood. The two layers are stick together through adhesive epoxy resin adhesive. The excitation and sensing element is a PZT-5A piezoelectric sensor. The diameter of the piezoelectric sensor is 8mm, and the thickness is.4mm. The experimental equipment is aircraft structure health monitoring system. The arrangement of the piezoelectric sensors is shown in Fig. 3, totalling 12, labelled as PZT 1, and PZT 2,..., PZT 12 from left to right, from bottom to top, the distance between the centre points of the two adjacent piezoelectric sensors is 15mm, and the centre of the outermost piezoelectric sensor is 2mm and 35mm from the boundary of the specimen. Building a two-dimensional Cartesian coordinate system in the specimen, which the PZT 1 piezoelectric sensor as the origin, the 4

5 horizontal direction from PZT 1 to PZT 4 as the x axis, the vertical direction from PZT 1 to PZT 9 as the y axis. As shown in Fig adhesive failures labelled A to G are applied on the structure by bonding a mass block respectively. The position of these adhesive failures is shown in Table 1. (a) Experimental system (b) Sketch map of piezoelectric sensor, simulated adhesive failure and rectangular coordinate system Fig. 3 The validation experiment system of the method Table 1 The position of the simulated adhesive failures Simulated adhesive failures x-coordinate/mm y-coordinate/mm A 15 1 B 19 7 C 37 8 D 1 23 E 23 3 F G In the experiment, setting the aviation structural health monitoring system in active mode, the excitation signal is the modulated five-cycle sine burst. The centre frequency of the excitation signal is 1 khz and the amplitude is ±7 volts. The sampling rate is 1 MHz and the sampling length is 5 samples including 1 pre-samples. The trigger voltage is 6 volts. The whole experiment process is as follows: first, in the structural health condition, the collected sensing Lamb wave response signal as the health reference signals; pasting a mass at the position of Table 1 in turn, which can changing the structural rigidity of the local area, and causing the Lamb wave scattering, simulating the air transport pallet adhesive failure [ ]. The corresponding Lamb wave signals are acquired as the on-line monitoring signals. 3.2 Location analysis of typical adhesive failure signals Take simulated adhesive failure D as an example. Fig. 4 shows the structural health condition, PZT 1 piezoelectric sensor as the excitation element, PZT 4, PZT 6, PZT 8, PZT 9, PZT 1, PZT 11 and PZT 12 piezoelectric sensor as the sensing element of the channel 1 to channel 7 health reference signals. Fig. 5 shown the on-line monitoring signals for simulated adhesive failure D when the PZT 1 piezoelectric sensor acts as an actuating element. In order to signal display, here gives only PZT 1 piezoelectric sensor as the sensing signals of the 7 excitationsensing channels when the excitation element, and a separate display excitation-sensing channel 4 sensing signal (PZT 1 excitation, PZT 9 sensing), as for comparing the differences between the reference signal and health monitoring signals. 5

6 15 Excitation-sensing Amplitude(V) Fig. 4 The health reference signals 15 Excitation-sensing Amplitude(V) Fig. 5 The on-line monitoring signals of adhesive failure D Calculating the damage index of each excitation-sensing channel by Eq. (1), as shown in Fig. 6. It can be seen from Fig. 6, No. 4 channel (PZT 1 excitation, PZT 9 sensing) damage index (.3) affected by the D simulation adhesive failure maximum, followed by the No. 1 channel (PZT 2 excitation, PZT 7 sensing), which is correspond to the D simulation adhesive failure position (1mm, 23mm). The damage index of No. 1 channel (PZT 1 excitation, PZT 4 sensing) is small as for it is far from the D simulation adhesive failure position Fig. 6 The damage index of each excitation-sensing channel Setting the coordinate resolution of 1mm, the structural damage probability image of the monitored area can be obtained by the damage probability imaging method described in section 2.2, as shown in Fig. 7. 6

7 The structural damage probability image x -coordinate(cm) Fig. 7 Structural damage probability image of simulated adhesive failure D Using the probability coordinate weighting algorithm of Eq. (5), the position coordinates of simulated adhesive failure D are calculated as (2.96cm, 23.37cm), and the distance error is 2.cm. 3.3 Verification result According to the above implementation process of air transport pallet adhesive failure, the adhesive failure localization results are shown in Table 2, it can be seen that the damage probability imaging method can effectively locate the air transport pallet adhesive failure, and distance error is less than 2cm. Table 2 Localization results of all adhesive failures Simulated adhesive failures 4. Conclusion Actual coordinate /cm Imaging localization results /cm A (15, 1) (16.71, 9.81) 1.7 B (19, 7) (2.31, 8.51) 2. C (37, 8) (35.65, 8.13) 1.4 D (1, 23) (2.96, 23.37) 2. E (23, 3) (22.28, 3.17).7 F (41, 19) (39.71, 19.79) 1.5 G (13, 42) (14.56, 42.5) 1.6 Distance error /cm In this paper, the damage probability imaging method is used in the air transport pallet adhesive failure. The method does not depend on the signal propagation model, compared with other damage imaging method is easier to damage location in this kind of composite structure for air transport pallet. The distance error is less than 2cm in this paper. Acknowledgements This work is supported by the National Natural Science Foundation of China (Grant No ). The work also partially benefited from Nanjing University of Aeronautics & Astronautics. The authors thank Professor Shenfang Yuan and Lei Qiu. References 1. Zhang J Y, Han D, Wu W J, Composite Structures for New Generation Large Commercial Jet. Acta Aeronautica et Astronautica Sinica, 28, 29(3): Rui M Y, Chen S Y, Wang J H, Study on Application of Container Pallet System in Aviation Transportation. Logistics Technology(Equipment), 215, 34(11):

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