The Study on Paper Board Thickness Measurement by Using Data Fusion

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1 The Study on Paper Board Thickness Measurement by Using Data Fusion Lianhua Hu 1, Xin ping Li 1,2, Wei Tang 1, Qinghong Liu 3 1 School of Light Industry and Energy Shaanxi University of Science & Technology Xi an, Shaanxi, China 2 Shaanxi Key Laboratory on Paper Technology and Specialty Papers Xi an, Shaanxi, China 3 China Petroleum Pipeline Bureau Tianjin Design Institute Tianjin, China Abstract This paper presents a new method of using a multi-output data fusion from a laser displacement sensor (LDS) to reduce vibration error for online noncontact paper board thickness measurement. A data-in-data-out fusion model for the thickness measuring was proposed. And a revised clustering method for fusing the canner gap data was presented. An EMD decomposition algorithm was used to analyze the high frequency noise. In order to find a desired filtering algorithm, five different filter designs were compared. After the comparison, sliding filter and Kalman filter were selected for noise removal to the original signal data from the LDS. In the fourth section, the revised clustering method was compared to an average algorithm for calculating the offset curve. Based on the proposed fusion model and revised clustering method, the fusion steps for online thickness measurement have been defined. Experimental results show the effectiveness of this method, and the thickness online measuring relative average error was less than ±4% at the range of 150μm -1500μm. Keywords - ponent; format; style; styling; insert I. INTRODUCTION The thickness of the paper directly affects the subsequent printing quality. Paper and packaging industry has strict standards to the paper thickness. The contact caliper is relatively mature, but it might scratch paper. Using laser displacement sensor (LDS) to measure the thickness of the paper is a new method for paper board thickness measurement. It has the advantages of non-contact measurement and less chance of paper scratch [1]. The disadvantage is that thickness measurement accuracy is affected by mechanical vibrations. The online paper board thickness measurement method is shown in Figure 1. The LDS is installed in the sensor box of the scanner. The LDS output A is the distance from the LDS to the boss when there is no paper board in park area. A is the LDS output when the sensor box is moving above paper board in the working area. It is the distance between the LDS and the paper board. Then the thickness of paper board (C) can be obtained. C=A-A (1) The mechanical vibrations are inevitable when the LDS working on the paper machine. The sources of the vibration come from the machine direction (MD) and cross machine direction (CD) and vertical direction of the paper (Z). In the direction of MD, the vibration is reciprocating swing around linear bearing center. In the direction of CD, the sensor box is driven by the cog belt, moving to and fro along a straight line rail. The expansion of cog belt will cause the dislocation of sensor box. The unparalleled straight line rail causes the sensor box vibrating in Z direction. Figure 1. Paper thickness measurement schematic diagram Figure 2. Scanner gap curve of 14 times records In order to test the mechanical vibrations of the scanner we recorded 14 gap data from LDS when the sensor box DOI /IJSSST.a.16.5B ISSN: x online, print

2 moving without paper from parking point to right limit point. The every gap data is marked as x in figure 2. Figure 2 shows that the scanner gap points are divergent, but overall trend is consistent, with some relatively dense converging centers at some places. The converging points are mostly close to the true gap value. And the vibration error is about -100μm-60μm, it is one of the major factors for paper board thickness measurement error. The online paper thickness measurement should be compensated by subtracting the vibration error curve. Filtering algorithm is generally used to eliminate the vibration error. The average algorithm is the most commonly used one. But in the LDS measurement for paper board thickness, average algorithm can't satisfy the requirement for thickness measurement. Data fusion technology in nearly 40 years has achieved rapid development, and it is widely used in industry production line [2-4]. Alex Rodriguez proposed a clustering method via fast searching and density peak detection [5]. We revised the clustering method and combine data fusion technology to eliminate the vibration error of the LDS thickness measurement. Our study found that it has an obvious advantage than the average algorithm. The structure of the paper is organized as follow: general introduction of the thickness measurement of paper board. A revised clustering method of finding density peaks is presented to calculate the average gap curve for reducing the vibration error which has been discussed in section II. The traditional ways of signal filtering has been discussed and compared in section III. Section IV, the revised clustering method, is used to find the scanner gap curve, and the data fusion model for online paper thickness measuring has been presented. The paper has been concluded in section V. II. REVISED CLUSTERING METHOD The biggest advantage of Alex Rodriguez algorithm [5] is to quickly find a wide range of local cluster center. The amount of calculation exponentially increase when the searching area increase. For industrial data fusion it is necessary to quickly find the high reliability data and catch the characteristic information of abnormal data among redundant data. Based on Alex Rodriguez s algorithm, we assume that a relatively small range data set i. It s scope of radius is r. Average distance between any two points within a range of radius is d A. There exists a highest credibility data T i and a largest abnormal degree data F i in redundant information. Local density function is defined as: i ( d ij k * d A) (2) j Among: r N; k*d A Distance threshold; (x) =1,x<0;otherwise (x) =0; k>0,k R T i is defined as: T i i max (3) i F i is defined as: Fi imaxd ij (4) Here we take d A *k as distance threshold, that makes the revised clustering method have the adaptability and adjustability. And the density function calculation can be greatly reduced by setting the range of radius r. The algorithm diagram of revised clustering method is shown in Figure 3. Figure 3. Revised clustering method algorithm diagram III. LDS SIGNAL ANALYZING AND FILTERING A. LDS Signal Analyzing The frequency component of noise signal can be observed with EMD decomposition method. Each frequency band noise conditions can be judged according to the results of the EMD separation [6]. Figure 4. EMD result of LDS signal DOI /IJSSST.a.16.5B ISSN: x online, print

3 The EMD is widely used in mechanical signal processing and fault diagnosis [7]. We take a LDS output curve from figure 2 and analyzing it with the EMD decomposition. The decomposed frequency curves can be observed as shown in figure 4. It can be seen from the figure 4 that the LDS output signal contains the imf1 high frequency component. The imf1 is originated by the three direction mechanical vibration during the process of sensor box scanning. Therefore, it is necessary to perform a high frequency denoising process during the LDS thickness measurement. B. LDS Signal Filtering In order to verify and optimizing the filtering effect, sliding average filter, frequency domain low-pass filter, median filter, Kalman filter and EMD decomposition are compared. The result can be seen in figure 5. A filter algorithm should only require a relative small computation power for industry applications, so the sliding average filter and Kalman filter would be an better algorithms to remove high frequency noise of LDS. IV. THICKNESS MEASUREMENT After the signal filtering of mechanical vibration, the next important step is to construct the vibration compensation curve. The revised clustering method and the average method are used to calculate the viration compensation curve. And a data fusion model is presented in this section. A. Gap compensation curve calculating Matlab simulation algorithm is used for the revised clustering method, take r=1, k=1. The calculated compensation curve is shown in figure 6. Figure 6(a) is the result of the entire profile of the two algorithms; figure 6(b) is the detail view of the 800mm-1150mm CD position. As can be seen from figure 6(b), the curve of the revised clustering algorithm is more converging to the data center than the average algorithm. The revised clustering algorithm can better reflect its advantages when the mechanical clearance and the amplitude of each direction increase after the scanner running for a long time. Figure 5. Comparison of denoising results It can be seen from the simulation results that all the filtering methods have certain effects on the noise signal reduction. Frequency domain low-pass filter has poor signal following at the initial start point. Kalman filter has a processing delay, but good signal following. Sliding average filtering performs very well because of its equivalence as a low pass filter. Median filtering is a nonlinear process with very good following performance, but has local spikes. The curve of EMD decomposition is relatively smooth after removing the high frequency signal, but with large deviation where the signal changes intensely. Figure 6. Comparison of denoising results B. Compensation effect verification In order to verify the effect of vibration compensation curves, a not filtering curve was randomly selected from DOI /IJSSST.a.16.5B ISSN: x online, print

4 figure 2 to subtract the compensation curves. The result of the subtraction is desired to zero. The simulation results are shown in figure 7. 5) Output the thickness measurement results. According to the above described procedure, the thickness of the scanner gap is measured, and the results are shown in Figure 9. Figure 9(a) is the result of original LDS curve subtracting the vibration compensation curve from the revised lustering algorithm. In figure 9(b) the LDS is filtered by sliding filter and then subtract the vibration compensation curve. Kalman filter is used to compare to the sliding filter in figure 9(c). Figure 7. Comparison of compensation effects In the entire CD profile, the biggest error of revised clustering method is smaller than average algorithm. The range of revised clustering method compensation error is from -15μm to +15μm, and the range of average algorithm compensation error is -30μm to +15μm. Their standard deviation is and respectively. The performance of revised clustering algorithm is improved by 51.6% compared with average algorithm. Then the other curves were selected for testing the compensation effect. Similar results were obtained. It is proved that the revised clustering algorithm proposed in section II is effective. C. Tickness measurement data fusion The fusion process of thickness measurement is from data to data. Combined with the data fusion model of Dasarathy [8], the thickness measurement data fusion model is as figure 8 shown. Figure 8. Thcikness measuremen data fusion model According to figure 8 the thickness measuring steps are as follows: 1) Filtering the original LDS data of the scanner gap; 2) Using revised clustering algorithm to calculate the gap compensation curve; 3) Filtering the online LDS data; 4) Using the online data curve to subtract gap compensation curve; Figure 9. Online gap measuring results Figure 9 shows that the sliding filter and the Kalman filter can reduce the online thickness measurement error from ±20μm to ±5μm. It can satisfy the requirements of actual paper board thickness measurement with a 10μm resolution. The standard deviations of figure 9 are shown in table I. TABLE I STANDARD DEVIATIONS OF FIGURE 9. No. Curve Standard Deviation 1 Original Sliding filter Kalman filter The standard deviation in table I proved that the Kalman filter is better than sliding filter in online paper board thickness measurement. And these two kinds of filter are simple and easy to realize. D. LDS Tickness measurement application We put the 1163μm board paper into the scanner and the figure 10 is the measuring curve. It can be seen that the actual test error of the two filter algorithms are in the range of ±40μm. In the end, for the range of 150μm -1500μm paper board thickness, the measured relative average error is less than ±4%, which can meet the requirement of the paper board thickness measurement precision. DOI /IJSSST.a.16.5B ISSN: x online, print

5 ACKNOWLEDGMENT This work was supported by grants from the Weiyang District of Xi'an Science and Technology Project (201410) and Scientific Research Fund of Shaanxi University of Science and Technology (Doctoral Fund, Research on Data Fusion Technology in Paper Industry, BJ15-19). Figure 10. Online gap measuring results V. CONCLUSION Mechanical vibrations can cause big measurement error for LDS caliper. In this paper, the EMD decomposition was used to analyze the high frequency component of the LDS signal. And five different types of normal filters were compared for finding simple and effective filtering algorithms. The revised clustering method was proposed to deal with the scanner offset curve. And we presented a datain-data-out data fusion model for online paper thickness measurement. The proposed method and model were applied in LDS thickness measurement. The test results show that the measuring relative average error was less than ±4% at the range of 150μm-1500μm. In conclusion, the experimental results show the benefits of the proposed method and model can be used to improve noncontact paper board thickness measurement accuracy. REFERENCES [1] By S. Nuyan, J. Graeffe, H. Moisio, Recent advances in online caliper measurement, PULP & PAPER CANADA, vol. 108, No. 11, 38-43, [2] M.S. Safizadeh, S.K. Latifi, Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell, Information Fusion, vol. 18, No. 07, 1-8, 2014 [3] Xiaoxin Wang, Hongli Hu, Aimin Zhang, Concentration measurement of three-phase flow based on multi-sensor data fusion using adaptive fuzzy inference system, Flow Measurement and Instrumentation, vol. 39, No. 10, pp. 1-8, [4] De Vin L J, Ng A H C, Oscarsson J, et al, Information fusion for simulation based decision support in manufacturing,. Robotics and Computer-Integrated Manufacturing, vol. 22, No. 05, pp , [5] Rodriguez Alex, Laio Alessandro, Clustering by fast search and find of density peaks, Science, vol. 344, No. 6191, pp , [6] Biao Huang, Guozheng Yan, Analysis of the characteristics of gastrointestinal motility based on Hilbert-Huang transform method, High Technology Letters, No. 14, pp , [7] Li H L, Deng x Y-Dai H L, Structural Damage Detection Using the Combination Method of EMD and Wavelet Analysis, Mechanical Systems and Signal Processing, No. 211, pp ,2007. [8] Bedworth M, O'Brien J. The Omnibus model: a new model of data fusion?, IEEE Aerospace and Electronic Systems Magazine, vol. 15, No. 04, pp , DOI /IJSSST.a.16.5B ISSN: x online, print

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