Patent Application: US-60653396-A

Abstract:
a method of determining the inflation pressure of one or more pneumatic tires on a moving vehicle having a plurality of n wheels fitted with tires by performing a set - up procedure comprising , for a range of tire inflation set - up procedure comprising , for a range of tire inflation pressures including all the vehicle tires at their scheduled inflation pressure and combinations of one or more tires at a range of pressures below their scheduled and for a range of vehicle speeds and driving conditions such as accelerating , braking , straight - ahead driving and cornering , deriving for each of the wheels a speed value cn proportion to the wheel angular velocity , determining and saving the relationship of the set of set - up speed values to each related tire pressure , and subsequently in normal driving monitoring at intervals of time the wheel speed signals of the wheels on the vehicle , deriving an equivalent set of normal driving speed values based on these monitored wheel speed signals , obtaining the associated tire inflation pressures . the method may utilize a trainable information processing system such as a neural network to map the speed values onto the set of tire inflation pressures . the method may particularly be applied to detecting a deflated tire on a vehicle .

Description:
the passenger car k shown in fig1 is a saloon model having four wheels w1 - w4 located at the front left side , front right side , rear left side and rear right side of the vehicle respectively . the car was fitted with an industry standard electronic antilock braking system ( abs ) utilizing at each of the four wheels w1 - w4 , a wheel signal generator producing a signal comprising 96 pulses for each revolution of the wheel . an antilocking braking system 1 has a pulse generator or wheel speed signal means associated therewith , which transmits the pulse to a microprocessor 2 comprising computing means 3 and data storage means 4 ( see fig1 ). the car was additionally fitted with computerised equipment for counting the number of pulses c1 - c4 produced from the wheel signal generators of each wheel w1 - w4 , respectively , in consecutive 5 - second periods . the car was also provided with means for storing this data and also separately inputting and storing the inflation pressures of each of the tires . in a set up procedure , the car was driven through a complete range of normal speeds and driving conditions such as straight ahead driving , cornering left and right , accelerating and braking . this was done with all four tires at their scheduled inflation pressure of 2 . 4 bar and repeated with each of the four tires in turn reduced in decrements of 0 . 2 bar down to an inflation pressure of 1 . 6 bar while the other three were maintained at the scheduled pressure . accordingly after completion of the set up procedure the storage means had stored in grouped data for each 5 - second period the instant wheel signal pulse counts c1 - c4 of each wheel w1 - w4 and the instant inflation pressure of each tire on wheels w1 - w4 . subsequently , in normal drawing , the four wheel speed signal pulse counts c1 - c4 in each consecutive 5 - second period were again monitored and compared by electronic means with the stored signal pulse counts from the set up procedure . when there was found a matching set of stored pulse counts in which each wheel count corresponded to the monitored wheel count of the same wheel , then in the event that one of the stored tire inflation pressures in that data group was a pressure of less than the schedule inflation pressure of 2 . 4 bar , then an alarm was activated to warn the driver of the car that a deflated tire had been detected . in a second , and preferred , embodiment of the present invention the stored wheel signal pulse count data was used to calculate eight intermediary set up speed parameters cp1 - cp8 defined as follows : it can be seen from the above definitions that cp1 is a parameter which is directly related to the average speed of the vehicle whereas cp2 - cp5 are parameters which are related also to the individual wheel speeds . parameters cp6 - cp8 are related to the differences in respectively front and rear wheels , left side and right side wheels and diagonally opposed pairs of wheels . these eight intermediary set - up speed parameters were used to train a trainable information processing system to map the speed values to the inflation pressures of the four tires . the trainable information processing system used was a neural network of the type known in the art as a multi - layer perceptron network . the perceptron network had an input layer comprising eight neurodes , each corresponding to one of the eight set up speed parameters , two hidden layers of neurodes and an output layer of four neurodes mapping to the four inflation pressures of the tires . the two hidden layers of neurodes had respectively 5 and 4 neurodes each . in each of the neurodes of the neural network the transfer function used to translate the input stimulus into the output response signal was one of the well - known sigmoid function type based on a hyperbolic tangent function tanh . the multi - layer perceptron network used was one of the many commercially available neural network systems which are commonly supplied with training routines enabling the network to learn specific information processing tasks . using such a training routine the multi - layer perceptron network was trained to predict the four tire pressures at the output layer in response to the eight set up speed parameters cp1 - cp8 applied to the input layer . this training was carried out using half of the stored data relating to one deflated tire from the set up procedure selected randomly . after completion of training , the reliability of the neural network in predicting tire pressures was assessed using the whole of the stored data relating to one deflated tire . part of this reliability assessment is presented in table 1 . shown in table 1 are the calculated speed parameters cp1 - cp8 , the actual inflation pressure of the tire and the predicted tire pressure obtained by applying these speed parameters to the trained network . table 1 shows the results for 216 data sets of which 50 % ( i . e . 108 ) relate to a tire having a reduced inflation pressure of 1 . 6 bar while the remainder relate to a normally inflated tire at a pressure of 2 . 4 bar . the reliability of the trained neural network to distinguish between the normally inflated tire and the underinflated tire is illustrated in table 2 which shows the separation matrix of predicted tire pressures . for the purpose of ranking in table 2 , the predicted tire pressures of table 1 have been rounded up to the nearest 0 . 1 bar . thus , it can be seen from table 2 that from , the 108 sets of data from underinflated tires the network predicted that 102 data sets related to tires at 1 . 6 - 1 . 8 bar , 5 sets related to tires at 1 . 8 - 1 . 9 bar and one set related to a tire at 1 . 9 - 2 . 1 bar . similarly for the 108 sets of data from normally inflated tires , the network predicted that 99 sets related to tires at above 2 . 2 bar , 2 sets related to tires at 2 . 1 - 2 . 2 bar , 4 sets related to tires at 1 . 9 - 2 . 1 bar and 3 sets related to tires at 1 . 8 - 1 . 9 bar . thus the total number of correct predictions was 201 out of 216 . however , the ability of the trained neural network to distinguish between the two groups of data by the selection of appropriate prediction bands is clearly demonstrated . subsequently , the trained neural network was implemented in the method of the invention predicting the tire pressure from the corresponding eight normal driving parameters derived from the four wheel speed signal pulse counts c1 - c4 monitored in normal driving . the tire pressures of the wheels were then displayed on a digital readout . in another embodiment , the predicted tire pressures were compared with the scheduled tire pressures for the vehicle and in the event that a tire was found to be deflated , then an alarm was operated to warn the driver that a tire was deflated . by extracting the network weights for the neurodes of the trained network , the trained information processing system may be implemented on an untrainable network using conventional computer techniques in a vehicle - based micro - computer or equivalent processing device . while the method of the invention has been illustrated by the use of a multi - layer perceptron network , other types of neural network information processing system may be employed . these include , for example , the well known kohonen networks , radial basis function networks or indeed any network which permits the mapping of a set of parameters onto a set of states . also , it should be understood that the training regime described may be changed , for example by using a full range of pressures for each of the tires . furthermore , the above embodiments use a single training or learning process . it is also feasible to retrain either occasionally or each time the vehicle is driven although in the latter case precautions need to be taken to avoid calibrating out pressure errors . also while the above - described embodiment utilizes the eight parameters cp1 - 8 both in training the neural network and , subsequently , in applying the trained network to the monitoring of the tires in normal driving , it is possible to use the actual wheel speed signal counts in this regard . it has been found , however , that the use of the above - described parameters derived from the wheel speed signal counts greatly reduces the network training time and increases the accuracy of the predicted tire pressures . the method of the invention can be applied to any vehicle having two or more wheels including motorcycles , motor cars and multi - axled trucks having more than four wheels . in other embodiments , additional signals to the wheel speed values may be monitored . these include , for example , steering wheel position , shock absorber pressure , spring length or deflection or the operative status of the anti - lock braking system . in the case of a two wheeled motorcycle , monitoring of the suspension deflections is a particularly effective &# 34 ; speed &# 34 ; value to be monitored in addition to the speed value for each of the wheels . the use of such additional signals as additional inputs to a neural information processing system can assist in minimizing the training time and maximizing the accuracy of the trained network in the application of the method to different types of vehicle . the invention being thus described , it will be obvious that the same may be varied in many ways . such variations are not to be regarded as a departure from the spirit and scope of the invention , and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims . table 1__________________________________________________________________________ tyreparameters pressure ( bar ) data set cp2 cp3 cp4 cp5 cp1 × 10 . sup .- 4 cp6 cp7 cp8 actual predicted__________________________________________________________________________1 - 0 . 00218 - 0 . 00218 0 . 00238 0 . 00198 0 . 74628 0 . 00871 - 0 . 0004 0 . 0004 2 . 4 2 . 40722 0 . 00166 0 . 00118 - 0 . 00118 - 0 . 00166 0 . 21135 - 0 . 00568 - 0 . 00095 0 . 0 2 . 4 2 . 40753 - 0 . 00213 - 0 . 00226 0 . 0022 0 . 0022 0 . 71747 0 . 00878 - 0 . 00014 - 0 . 00014 2 . 4 2 . 407364 - 0 . 00524 - 0 . 00071 0 . 0033 0 . 00265 0 . 77315 0 . 0119 0 . 00388 0 . 00517 1 . 6 1 . 595315 - 0 . 00594 - 0 . 00228 0 . 00359 0 . 00463 0 . 76645 0 . 01644 0 . 0047 0 . 00261 11 . 6 1 . 603266 - 0 . 00532 - 0 . 00142 0 . 00316 0 . 00358 0 . 71957 0 . 01348 0 . 00431 0 . 00347 1 . 6 1 . 595647 - 0 . 01385 0 . 00727 - 0 . 00891 0 . 01549 0 . 36475 0 . 01316 0 . 04551 - 0 . 00329 2 . 4 2 . 355368 - 0 . 00555 - 0 . 00154 0 . 00278 0 . 00432 0 . 6482 0 . 01419 0 . 00555 0 . 00247 1 . 6 1 . 584899 - 0 . 0053 - 0 . 00222 0 . 00344 0 . 00408 0 . 77767 0 . 01504 0 . 00373 0 . 00244 1 . 6 1 . 6135110 - 0 . 00373 - 0 . 00261 0 . 00218 0 . 00415 0 . 71015 0 . 01267 0 . 0031 - 0 . 00084 2 . 4 2 . 4046711 - 0 . 00365 - 0 . 00319 0 . 0028 0 . 00403 0 . 65143 0 . 01366 0 . 00169 - 0 . 00077 2 . 4 2 . 3935812 - 0 . 0027 - 0 . 00168 0 . 00197 0 . 00241 0 . 68525 0 . 00876 0 . 00146 0 . 00058 2 . 4 2 . 406613 - 0 . 00538 - 0 . 00099 0 . 00284 0 . 00353 0 . 72938 0 . 01275 0 . 00507 0 . 0037 1 . 6 1 . 5954114 - 0 . 00532 - 0 . 00166 0 . 00316 0 . 00383 0 . 6011 0 . 01397 0 . 00433 0 . 00299 1 . 6 1 . 5965915 - 0 . 00217 - 0 . 00217 0 . 00275 0 . 00159 0 . 6913 0 . 00868 - 0 . 00116 0 . 00116 2 . 4 2 . 4010816 - 0 . 0036 - 0 . 00277 0 . 00291 0 . 00346 0 . 7227 0 . 01273 0 . 00138 0 . 00028 2 . 4 2 . 4042917 - 0 . 01484 0 . 01105 - 0 . 00947 0 . 01326 0 . 3168 0 . 00758 0 . 04861 0 . 00316 1 . 6 1 . 6128618 - 0 . 00286 - 0 . 00233 0 . 00233 0 . 00286 0 . 75095 0 . 01039 0 . 00107 - 0 . 0 2 . 4 2 . 4071419 - 0 . 00215 - 0 . 00215 0 . 00268 0 . 00162 0 . 66298 0 . 0086 - 0 . 00106 0 . 00106 2 . 4 2 . 4032420 - 0 . 00395 - 0 . 00286 0 . 00317 0 . 00364 0 . 64575 0 . 01363 0 . 00155 0 . 00062 2 . 4 2 . 3886121 - 0 . 00552 - 0 . 00169 0 . 00322 0 . 00399 0 . 652 0 . 01442 0 . 0046 0 . 00307 1 . 6 1 . 5972822 - 0 . 00468 - 0 . 00101 0 . 0033 0 . 00239 0 . 76412 0 . 01139 0 . 00275 0 . 00458 1 . 6 1 . 595323 - 0 . 00277 - 0 . 0021 0 . 00203 0 . 00283 0 . 75002 0 . 00973 0 . 00147 - 0 . 00013 2 . 4 2 . 407324 - 0 . 00526 - 0 . 00108 0 . 00283 0 . 00351 0 . 741 0 . 01269 0 . 00486 0 . 00351 1 . 6 1 . 5953925 - 0 . 00211 - 0 . 00195 0 . 00226 0 . 00179 0 . 64125 0 . 00811 - 0 . 00031 0 . 00062 2 . 4 2 . 4067126 - 0 . 00274 - 0 . 00631 0 . 00911 - 0 . 00006 0 . 44747 0 . 0181 - 0 . 01274 0 . 00559 1 . 6 1 . 8149227 - 0 . 0144 0 . 00643 - 0 . 00617 0 . 01414 0 . 389 0 . 01594 0 . 04113 0 . 00051 1 . 6 1 . 6610128 - 0 . 00253 - 0 . 00212 0 . 00219 0 . 00247 0 . 72008 0 . 0093 0 . 00069 0 . 00014 2 . 4 2 . 4072329 - 0 . 0025 - 0 . 00154 0 . 00188 0 . 00216 0 . 72988 0 . 00808 0 . 00123 0 . 00068 2 . 4 2 . 4065830 - 0 . 00519 - 0 . 00109 0 . 00229 0 . 00399 0 . 70872 0 . 01256 0 . 00578 0 . 0024 1 . 6 1 . 606831 - 0 . 0177 0 . 01237 - 0 . 01317 0 . 01851 0 . 30933 0 . 01067 0 . 06175 - 0 . 00162 2 . 4 2 . 3591132 - 0 . 00374 - 0 . 00302 0 . 00302 0 . 00374 0 . 6944 0 . 01354 0 . 00144 0 . 0 2 . 4 2 . 433 - 0 . 00203 - 0 . 00216 0 . 00257 0 . 00162 0 . 7401 0 . 00838 - 0 . 00108 0 . 00081 2 . 4 2 . 4062534 - 0 . 00494 - 0 . 00088 0 . 00291 0 . 00291 0 . 76452 0 . 01164 0 . 00405 0 . 00405 1 . 6 1 . 5953135 - 0 . 00594 - 0 . 00181 0 . 00297 0 . 00478 0 . 774 0 . 0155 0 . 00594 0 . 00233 1 . 6 1 . 6087136 - 0 . 00564 - 0 . 00118 0 . 00341 0 . 00341 0 . 74032 0 . 01364 0 . 00446 0 . 00446 1 . 6 1 . 5955737 - 0 . 00637 - 0 . 00044 0 . 00133 0 . 00548 0 . 33755 0 . 01363 0 . 01007 0 . 00178 1 . 6 1 . 550138 - 0 . 0032 - 0 . 00195 0 . 00167 0 . 00348 0 . 7191 0 . 01029 0 . 00306 - 0 . 00056 2 . 4 2 . 4071239 - 0 . 00803 - 0 . 00836 0 . 00869 0 . 0077 0 . 30505 0 . 03278 - 0 . 00131 0 . 00066 2 . 4 2 . 3632140 - 0 . 00327 - 0 . 0022 0 . 00207 0 . 00341 0 . 74885 0 . 01095 0 . 0024 - 0 . 00027 2 . 4 2 . 4070341 - 0 . 00546 - 0 . 00223 0 . 00359 0 . 0041 0 . 77347 0 . 01539 0 . 00375 0 . 00272 1 . 6 11 . 6009942 - 0 . 00626 - 0 . 00274 0 . 0043 0 . 00471 0 . 73857 0 . 01801 0 . 00393 0 . 00311 1 . 6 1 . 6437343 - 0 . 00289 - 0 . 00233 0 . 00198 0 . 00324 0 . 71812 0 . 01044 0 . 00181 - 0 . 0007 2 . 4 2 . 4070544 - 0 . 00514 - 0 . 00092 0 . 0029 0 . 00317 0 . 73392 0 . 01213 0 . 0045 0 . 00395 1 . 6 1 . 5953545 - 0 . 00519 - 0 . 00148 0 . 00292 0 . 00374 0 . 72793 0 . 01333 0 . 00453 0 . 00288 1 . 6 1 . 5957246 - 0 . 00293 - 0 . 00266 0 . 00279 0 . 00279 0 . 73445 0 . 01116 0 . 00027 0 . 00027 2 . 4 2 . 4065347 - 0 . 00416 0 . 00059 0 . 00161 0 . 00195 0 . 58935 0 . 00713 0 . 00509 0 . 00441 1 . 6 1 . 5951648 - 0 . 00276 - 0 . 0018 0 . 00201 0 . 00255 0 . 73448 0 . 00912 0 . 0015 0 . 00041 2 . 4 2 . 4070449 - 0 . 0062 - 0 . 002 0 . 00347 0 . 00474 0 . 78602 0 . 01641 0 . 00547 0 . 00293 1 . 6 1 . 5985950 - 0 . 00505 - 0 . 00154 0 . 00351 0 . 00309 0 . 7125 0 . 01319 0 . 00309 0 . 00393 1 . 6 1 . 595651 - 0 . 00331 - 0 . 00249 0 . 00249 0 . 00331 0 . 7241 0 . 0116 0 . 00166 0 . 0 2 . 4 2 . 4063352 - 0 . 00323 - 0 . 00282 0 . 00282 0 . 00323 0 . 72705 0 . 0121 0 . 00083 - 0 . 0 2 . 4 2 . 405853 - 0 . 00583 - 0 . 00222 0 . 00384 0 . 00422 0 . 77567 0 . 01612 0 . 004 0 . 00322 1 . 6 1 . 5978554 - 0 . 00336 - 0 . 0025 0 . 0025 0 . 00336 0 . 58075 0 . 01171 0 . 00172 0 . 0 2 . 4 2 . 4000855 - 0 . 00216 - 0 . 00268 0 . 003 0 . 00184 0 . 77382 0 . 00969 - 0 . 00168 0 . 00065 2 . 4 2 . 4065556 - 0 . 00595 - 0 . 00109 0 . 00273 0 . 00431 0 . 76007 0 . 01408 0 . 00645 0 . 00329 1 . 6 1 . 5956157 - 0 . 00519 - 0 . 00171 0 . 00323 0 . 00367 0 . 68862 0 . 0138 0 . 00392 0 . 00305 1 . 6 1 . 5958158 - 0 . 00247 - 0 . 00218 0 . 00225 0 . 0024 0 . 69858 0 . 0093 0 . 00043 0 . 00014 2 . 4 2 . 4071659 - 0 . 0188 0 . 016 - 0 . 01569 0 . 01849 0 . 32185 0 . 00559 0 . 06898 0 . 00062 2 . 4 2 . 3494260 - 0 . 00352 0 . 0006 0 . 00168 0 . 00124 0 . 46198 0 . 00584 0 . 00368 0 . 00455 1 . 6 1 . 5950361 0 . 00041 - 0 . 00921 0 . 01167 - 0 . 00287 0 . 42628 0 . 01759 - 0 . 02416 0 . 00493 2 . 4 1 . 8418462 - 0 . 00367 0 . 00037 0 . 00202 0 . 00128 0 . 5457 0 . 0066 0 . 0033 0 . 00476 1 . 6 1 . 5952463 - 0 . 00237 - 0 . 00156 0 . 00101 0 . 00291 0 . 73935 0 . 00784 0 . 00271 - 0 . 00108 2 . 4 2 . 4074764 - 0 . 00534 - 0 . 0009 0 . 00284 0 . 0034 0 . 72115 0 . 01248 0 . 00499 0 . 00388 1 . 6 1 . 5953965 - 0 . 00569 0 . 00045 - 0 . 00083 0 . 00608 0 . 39077 0 . 01049 0 . 01305 - 0 . 00077 2 . 4 2 . 3882866 - 0 . 00217 - 0 . 00244 0 . 0027 0 . 00191 0 . 75955 0 . 00922 - 0 . 00105 0 . 00053 2 . 4 2 . 4069767 - 0 . 00197 - 0 . 00149 0 . 00157 0 . 00189 0 . 62058 0 . 00693 0 . 00081 0 . 00016 2 . 4 2 . 4073468 - 0 . 00326 0 . 00133 0 . 00181 0 . 00012 0 . 41435 0 . 00386 0 . 0029 0 . 00627 1 . 6 1 . 5952769 - 0 . 00574 - 0 . 00169 0 . 00311 0 . 00432 0 . 66618 0 . 01486 0 . 00525 0 . 00285 1 . 6 1 . 5971270 - 0 . 0031 - 0 . 00229 0 . 0027 0 . 0027 0 . 7416 0 . 01079 0 . 00081 0 . 00081 2 . 4 2 . 4039771 - 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0 . 00351 - 0 . 00262 0 . 00314 0 . 00299 0 . 67743 0 . 01225 0 . 00074 0 . 00103 2 . 4 2 . 38561212 - 0 . 01414 0 . 00794 - 0 . 00929 0 . 01548 0 . 37135 0 . 01239 0 . 04686 - 0 . 00269 2 . 4 2 . 38248213 0 . 00043 0 . 00043 0 . 00043 - 0 . 00128 0 . 52723 - 0 . 00171 - 0 . 00171 0 . 00171 2 . 4 2 . 39883214 - 0 . 00172 - 0 . 00141 0 . 00149 0 . 00164 0 . 65527 0 . 00626 0 . 00046 0 . 00015 2 . 4 2 . 40742215 - 0 . 00285 - 0 . 00258 0 . 00251 0 . 00292 0 . 72763 0 . 01086 0 . 00069 - 0 . 00014 2 . 4 2 . 40688216 - 0 . 00291 - 0 . 00243 0 . 00291 0 . 00243 0 . 6182 0 . 01068 0 . 0 0 . 00097 2 . 4 2 . 39848__________________________________________________________________________ table 2______________________________________ predictedactual 1 . 6 + 1 . 8 + 1 . 9 + 2 . 1 + 2 . 2 + ______________________________________1 . 6 + 108 102 5 1 0 01 . 8 + 0 0 0 0 01 . 9 + 0 0 0 0 02 . 1 + 0 0 0 0 02 . 2 + 108 0 3 4 2 99______________________________________