3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015)

Transcription

1 3rd Iteratioal Coferece o Material, Mechaical ad Maufacturig Egieerig (IC3ME 205) A Sesor Parameter Calibratio Method of Tire Pressure Moitor System Based o Liear Regressio Techology Meilig Li,a, Xiaopeg Ji,b, Hao Liu, c *, Zhiqiag Wei,d College of Iformatio Sciece ad Egieerig, Ocea Uiversity of Chia,Qigdao,Shadog,26600,Chia a limeilig@ouc.edu.c, b jixiaopeg@ouc.edu.c, c liuhao@ouc.edu.c, d weizhiqiag@ouc.edu.c Keywords: A liear calibratio model; Liear regressio techology; Least square method; Sesor parameter calibratio; Tire Pressure Moitor System Abstract. The regular sesors that are used for TPMS (Tire Pressure Moitor System) are some iaccuracies for collectig parameters. Thus, a ew error correctio method is proposed to calibrate tire pressure values based o liear regressio techology. Ad based o the way a friedly iteractive TPMS is achieved. The parameters of tire pressure sesors are determied by the experimet. Ad sesor parameter calibratio is based o least square method to fit the values. Furthermore, a liear calibratio model is obtaied through cotiuous liear regressio aalysis. Fially, the optimal calibratio model corrects the raw data, so the use of the model greatly improves the accuracy of TPMS. Ad it greatly improves the stability ad flexibility of traditioal TPMS. The testig ad practice has proved the feasibility of the model. Itroductio Over the past two decades, alog with the ecoomic growth has brought huge demad for logistics ad other areas, the freight vehicle owership is growig. Accordig to statistics, early half of the freight vehicle accidet is due to tire faulty. Especially, the abormal tire pressure ofte leads to a major accidet []. I additio, improper tire iflatio pressure will dramatically icrease eergy cosumptio of a vehicle. TPMS (Tire Pressure Moitor System) ca esure that the workig status of the tire is maitaied withi the stadard rage. It moitors tire pressure, temperature ad other data [2]. Ad this prevets the accidets waitig to happe ad reduces eergy cosumptio. Oce a tire faulty is detected, TPMS will prompt or war users through pre-matchig ad simple cotrol equipmet [3]. Such simple cotrol equipmet presets two issues: first, the traditioal TPMS pressure sesor is iaccurate. Their built-i correctio factor is too simple for the high-pressure tires o heavy freight vehicles. Secod, the absece of user iteractio fuctio o a traditioal TPMS reduces the flexibility of the system. Moreover, it limits the further applicatios of the system [4]. Cosiderig the drawbacks of traditioal TPMS, a freight vehicle orieted high-precisio iteractive TPMS is proposed. Ad the ew TPMS utilizes mobile cellular etwork to trasmit data to the data ceter for further aalysis. We itroduced a liear regressio model to the sesors of TPMS for parameter calibratio. After that, a smartphoe is utilized to achieve better user iteractio. Due to strog processig performace, the smartphoe is equipped with a applicatio that implemets more advaced calibratio algorithm. Techical Framework A. System Applicatio Overview I our solutio, the TPMS moitors real-time tire pressure ad other data to esure the vehicle ca travel at a stadard tire pressure ad temperature. The sesor compoets are mouted o each tire valve stems exterally to measure the tire pressure ad tire temperature. A sesor wirelessly trasmits raw data to a commuicatio termial [3]. Sometimes, a relay trasmitter is ecessary for extedig the commuicatio rage o a log truck. After receivig the raw data from ay sesors, the commuicatio termial wirelessly trasmits formatted data to a Adroid-based smartphoe. Ay abormal tire pressure, oce it s detected, sets off the alarm both o the smartphoe ad the 205. The authors - Published by Atlatis Press 05

2 commuicatio termial. The applicatio will request a roadside assistace uder the permissio of users. The rescue ceter receives a rescue request ad verifies the iformatio such as tire type. The utilizatio of the smartphoe sigificatly exteds the iteractio feature of the TPMS. Durig the whole assistace procedures, users, rescue ceter ad rescue vehicle share ecessary data such as faulty vehicle positio, tire status or remaiig assistace time. Ad the system structure diagram is show i Fig.: B. Hardware ad Software Framework Fig.. System structure diagram The system hardware maily cosists of three hardware compoets: the tire pressure sesor, the commuicatio termial ad the relay trasmitter. Fig.2 presets two tire pressure sesors ad oe commuicatio termial. The bigger object is the commuicatio termial. The sesor amout depeds o wheel amout of the vehicle. Fig.2. Two tire pressure sesors ad oe commuicatio termial The compoet of tire pressure sesor is based o SP370 from Ifieo. We optimized the power maagemet to exted battery life. The metal shell ad the atea are combied together. Such a desig adapts to operatig coditio of the system. The commuicatio termial acquires power through the car cigarette lighter. All the hardware compoets are coected wirelessly. There are two commuicatio stages. I the first stage, the coectio betwee sesors ad commuicatio termial is oe-way from a sesor to termial through RF 433MHz. I this stage, a relay trasmitter may be ecessary for extedig the commuicatio rage. I the secod stage, the commuicatio termial is coected with a smartphoe through Bluetooth. The commuicatio termial trasmits ecrypted data to the smartphoe. The ecryptio is itroduced for itegrity checkig, ot for 052

3 security reaso. The mai fuctio of software is to moitor ad display the curret tire pressure ad tire temperature i real-time. I this paper, the applicatio realized calibratio algorithm, ad the calibrated data are accurately displayed. Applicatio cotiuously moitors the cotiuous state chage of tires, ad the early wars for tire abormal coditio. I additio, the applicatio provides users with roadside assistace ad other auxiliary services. Applicatio receives tire pressure data i the form of Bluetooth commuicatio, ad coducts sesor parameter calibratio. Accordig to the respective sesor which is mouted o each tire, users match o each tire pressure sesor ID. The the data of each tire is set to the itelliget termial iterface i the form of broadcast. The system stores the matched sesors, ad itelliget termial will be real-time moitorig the tire pressure, temperature ad other data of each tire. C. Sesor Calibratio Sesor calibratio is a process to build a mathematical model based o the measuremet priciple, ad a output value is obtaied correspodig to the differet iput value. Fially it ca get a sesor calibratio model. I order to make the collected tire pressure values ad the actual tire pressure values close as much as possible, regressio aalysis should be carried out cotiuously. Regressio aalysis ca be cosidered as a process of circulatio, which is based o sample data, ad use some statistical methods to solve the relatioship betwee variables [5]. This process is sometimes ecessary to be repeated several times util the model ca get to meet the assumptio ad properly fit the data. So tire pressure calibratio is far easier to achieve by usig hardware. I actual measuremet of tire pressure, there are radom errors i tire pressure values that are measured by the tire pressure sesors. This paper assumes that the error exists i a liear way, ad it obtais a liear regressio model by cotiuously collectig a large umber of tire pressure data ad coductig regressio aalysis. Sesor Calibratio A. Data Collectio I this paper, all the data is collected from static tires. For coveiece reaso, the tire is iflated to the maximum pressure. The the tire is deflated ad the chagig pressure values are recorded by multiple gauges ad the tire pressure sesor to be calibrated at the same time. Tire pressure data acquisitio step follows: First, multiple tire pressure gauges are utilized to record the actual tire pressure. The average measuremet is cosidered as a stadard measuremet, or groud truth. Both aalog ad digital display type of tire pressure gauge is utilized to avoid idividual error. Secod, durig the same deflatig procedure, the measuremets from the calibratig sesors are recorded. The recordig operatio is implemeted o the smartphoe applicatio. Accordig to the above steps, multiple sets of tire pressure data are collected. These data provide a powerful support for the followig regressio aalysis. Due to the limited space of the paper, we caot list the etire data. 5 sets of tire pressure data is show i Table. B. Data Aalysis Tire pressure data that is obtaied by the tire pressure sesors o the tire exteral presets radom errors, so it is required to obtai the sesor calibratio model to calibrate the data. A simple aalysis of data ca be draw, the true value is larger tha the sesor value about 00kpa, ad this meas that the value that is acquired by the sesor is the curret value of absolute pressure, while the aalog or digital measures the relative pressure of the tire at atmospheric pressure. Eve if the tire pressure sesor is exposed i a ope eviromet, it ca measure the pressure about 98 ~ 0kpa. I order to effectively calibrate sesor values, the least square method is used for liear fittig i this paper. 053

4 Table.. The tire pressure data collectio table No. Aalog values Digital values Stadard values Sesor values (kpa) (kpa) (kpa) (kpa) Ideally, the sample data should be collected as much as possible over a wide rage to improve accuracy. However, uder realistic coditios, we ca oly collect data withi a limited rage. I this paper, the sesor values are defied as the variable x, the stadard values are defied as variables y. They may satisfy prototype fuctio y = ax + b, so it is required to get the value of least-square solutios to satisfy the above data [6]. First, draw from the scatterplot i Fig.3, the black dots represet discrete poits. Secod, from the graphical poit of view, substatially discrete poits fall o a straight lie, so it is a liear relatioship betwee the measured value ad the true value of the tire pressure, ad it ca be determied by a liear fit to satisfy the relatioship betwee the discrete poits. It is assumed that a liear model, y = ax + b, is foud out, the the followig least square method is used to calculate the value of a ad b [7,8]. * x i N, supposig For sample data i ( ) ( ) 2 i i () D = y ax + b i= Whe the miimum value of D is foud, as i Eq., fittig level is the best state betwee the fittig lie ad the observatio of poit. Least squares priciple is determied to obtai the value of a ad b that ca get the miimum value of D, if the D is see as a fuctio of a ad b [9], accordig to the coditio of the multivariate fuctio that takes the value, the above problems are solved by solvig Eq.2, as follows: D = 2 [ yi ( axi + b) ] xi = 0 a i= D = 2 [ yi ( axi + b) ] = 0 b i= By solvig Eq.2, the value of a ad b ca be obtaied. Makig x= x, ca calculate Eq.3,eve if: i= i i= i (2) y = y, the we y a bx = 0 (3) 2 xy ax bx = 0 The simply for ease of accoutig, Eq.3 is switched to matrix form, ad elimiates, the we ca work out a ad b, as i Eq

5 a= y bx xy xy (4) b = 2 2 x ( x) As a result, the related parameters i the liear regressio equatio ca be obtaied accordig to the result of curve fittig. If the fittig results are ot satisfied, steps fittig may repeat may times. It ca be leared, a liear calibratio model of tire pressure values is that, f(x) = x Fittig curve is show i Fig.3 as solid blue lie, wherei the horizotal axis is the tire pressure value of sesor, the vertical axis represets the actual value of the stadard tire pressure. C. Model Validatio Fig.3. Liear fittig I order to verify the validity ad accuracy of the model, several groups of data were selected for holdout validatio. We list 20 groups of data. After puttig each group data ito a liear regressio model to be calculated, the calibratio values ad deviatios of tire pressure ca be obtaied. The Table 2 shows the compariso of the measured tire pressure data with the calibrated data. Table.2. Bias ad bias ratio after calibratio data No. Sesor Stadard calibratio Bias Bias values (kpa) values (kpa) values (kpa) ratio % % % % % % % % % % % % % % % % % % % 055

6 By calculatig ad comparig, tire pressure values that are calibrated ad the actual values that are measured by the tire gauge have deviatios withi 0kpa, ad the ratio of the deviatio is withi 3.6%, idicatig that the result of fittig is better, ad the degree of fittig is higher, ad liear correlatio is strog. Through experimetal verificatio, TPMS uses the above model to calibrate sesor values, ad gets accurate values i real time by usig pre-matchig ad self-developed tire pressure sesors. Ad the calibrated tire pressure is higher accuracy, ad TPMS ca be re-match ad get the sesor values, ad fially the calibratio model has practical value. Coclusios Based o least square liear regressio techology, a liear calibratio model is established to calibrate the parameter of tire pressure sesor through fittig the data ad cotiuous data aalysis. As a result, TPMS obtais the precise tire pressure by exteral sesors which are mouted o the wheel i real time. First, the measured tire pressure is calibrated to be the stadard tire pressure parameters by usig the sesor parameter calibratio model, ad it provides a effective ad reliable basis for collectig tire pressure data. Ad the tire pressure data that is calibrated by the calibratio model have reliability ad validity. Secod, the sesor parameter calibratio model greatly improves the measuremet accuracy ad iteractivity of TPMS through applyig the optimizatio algorithm. But the accuracy of the tire pressure data are slightly less tha, so a further research is the eed for more accurate calibratio of the model parameters, ad it lays the foudatio for future real-time moitorig tire pressure. Ackowledgmets This research is supported by Natioal Sciece Foudatio ( ), ad Shadog Provice Sciece ad Techology Developmet Pla (204GGX0005), ad Qigdao Strategic Emergig Idustries Developmet Programme ( hy). Refereces [] Yula Zhou, Yahog Zag, Yahog Li, Based o Multi-sesor Iformatio Fusio Algorithm of TPMS Research, Physics Procedia. 25 (202) [2] Haa M A, Hussai A, Mohamed A, et al, TPMS Data Aalysis for Ehacig Itelliget Vehicle Performace, Joural of Applied Scieces. 2008, 8(0). [3] Zemig Zhou, The Priciple ad Applicatio of Automobile Tire Pressure Detectio System, Agricultural Equipmet & Vehicle Egieerig. 2 (2009) [4] Wei Zhou, Jiawei Bao, Liaghua Dig, Noliear Correctio of Sesor Calibratio, Joural of Test & Measuremet Techology. 4 (203) [5] Zhipig Wag, Research o the sesor calibratio of pressure trasmitter based o the least square method, The 2th IEEE Iteratioal Coferece o Sigal Processig. (204) [6] Xiaogag Su, Xi Ya, Chih-Lig Tsai, Liear regressio, Wiley Iterdiscipliary Reviews Computatioal Statistics. 4 (202) [7] Leqiag Zou, Its simple applicatio of the priciple of least squares, Techology Iformatio. 23 (200) [8] Ximig Lv, Migyua Li, The least squares curve fittig of the Matlab, Ier Mogolia Uiversity for Natioalities. 24 (2009) [9] Jiaqi Liu, Jixi Yag, Chuya Wag, Evolutioary algorithm i least square method for curve fittig, Joural of Computer Applicatios. 30 (200)