A method of determining the inflation pressure of a tyre on a moving vehicle

Abstract: A method of determining the inflation pressure of one or more pneumatic tyres on a moving vehicle having a plurality of n wheels fitted with tyres characterised by performing a set-up procedure comprising, for a range of tyre inflation pressures including all the vehicle tyres at their scheduled inflation pressure and combinations of one or more tyres at a range of pressures below their scheduled pressure 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) proportional to the wheel angular velocity, determining and saving the relationship of the set of set-up speed values to each related tyre 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 tyre inflation pressures. The method may utilise a trainable information processing system such as a neural network to map the speed values onto the set of tyre inflation pressures. The method may particularly be applied to detecting a deflated tyre on a vehicle.

Description: This invention relates to a method of determining the inflation pressure of a tyre on a moving vehicle and more particularly to detecting a partially or totally deflated pneumatic tyre on a moving vehicle. Prior art methods of measuring tyre pressures on moving vehicles utilise radio transmitters associated with the rotating tyre and wheel, or communicating tubes with rotating seals at the vehicle hub which lead to fixed pressure sensors on the vehicle body. Such devices are expensive and/or complex in design. There also exist proposals to detect deflated tyres by comparing with the other tyres on the vehicle. One such system utilises wheel speed signals from the vehicle wheels such as for example the signals from anti-lock braking systems being fitted to an increasing proportion of modern vehicles. These recent deflation warning systems compare the vehicle wheel speed signals in various ways in order to attempt to identify the small increase in wheel speed caused by the extremely small change in the tyre rolling radius which occurs when pressure is lost from the tyre. There are two major problems which these systems have to overcome. The first problem is the avoidance of false warnings by distinguishing reliably between changes in wheel speed due to tyre deflation and those changes due to vehicle factors such as cornering, braking, accelerating and uneven or changing loads which can easily cause changes in speed signals which are very much larger than those due to tyre deflation. The second problem is to provide a warning in a sufficiently short period of time that the vehicle driver may take action to avoid further damage to the tyre or in an extreme case of deflation, an accident to the vehicle. The deflation warning system described in European Patent Publication 0 291 217 has largely overcome both of the major problems described above and provides for a four-wheeled vehicle a method which will successfully warn of a deflated tyre in most normal driving situations. However such a deflation warning system requires the prior determination of certain vehicle related constants which may be often specific only to one particular make and model of vehicle. Such constants can only be determined by monitoring of the vehicle wheel speeds under exhaustive set up procedures followed by complex analysis of the resultant data by expert statisticians and vehicle engineers. Such time and expense of refining the method is thus prohibitive of the development of a universal deflation warning system having maximum reliability and suitability for any vehicle. US-A-5 218 862 discloses a method of determining the inflation pressure of one or more pneumatic tires on a moving vehicle having a plurality of wheels fitted with tires comprising the steps of: performing a set-up procedure including, for a range of tire inflation pressures including all the vehicles tires at their scheduled inflation pressure and combinations of one or more tires at a range of pressures below their scheduled pressure and for a range of vehicle speeds and driving conditions to include accelerating, braking, straight-ahead driving and cornering, the steps of: deriving for each of the wheels a set-up speed value proportional to the wheel angular velocity, determining the relationship of the set of set-up speed values Cn to the set of tire pressures, storing in a data storage means the determined relationship of the set of set-up speed values to each related tire pressures, and subsequently in normal driving monitoring, in subsequent normal driving, 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, and obtaining the associated tire inflation pressures by using the determined relationship stored in the data storage means. A further comparable tire air-pressure reduction detection method is described in EP 0 652 121 A1 published after the priority date of the present application (state of the art according to Article 54 (3) and (4) EPC). However in order to achieve an acceptable level of accuracy such a system or method is required to continuously process an excessive amount of data and has not been widely adopted. Even the above systems however do not measure tyre pressures and when used to sense deflations require complex setting up, including for example determining complex vehicle related constants. It is an object of the present invention to overcome the disadvantages of the above prior art providing a system which is easily set up without the need to determine vehicle constants and also to give tyre pressure measurements. The object of the invention is achieved by the subject-matter of claim 1; further advantageous embodiments thereof are defined in the dependent claims. Further aspects of the invention will become apparent from the following description, by way of example only, of embodiments of the invention in conjunction with the following drawings in which: Figure 1 shows a schematic diagram of a passenger car showing the four wheels, for a first embodiment of the invention. The passenger car K shown in Figure 1 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 anti-lock braking system (ABS) utilising at each of the four wheels W1-W4 a wheel signal generator producing a signal comprising 96 pulses for each revolution of the wheel. 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 tyres. 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 tyres at their scheduled inflation pressure of 2.4 bar and repeated with each of the four tyres in turn reduced in decrements of 0.2 bar down to an inflation pressure of 1.6 bar whilst 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 tyre on wheels W1-W4. In accordance with a prior method subsequently in normal driving the four wheel speed signal pulse counts C1-C4 in each consecutive 5-second period would again be 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 tyre inflation pressures in that data group was a pressure of less than the schedule inflation pressure of 2.4 bar then an alarm would be activated to warn the driver of the car that a deflated tyre had been detected. However in the method 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:- CP1 = Average speed parameter A = (C1+C2+C3+C4)/4 CP2 = 1 - (C1/A) CP3 = 1 - (C2/A) CP4 = 1 - (C3/A) CP5 = 1 - (C4/A) CP6 = ((C1+C2) - (C3+C4))/A CP7 = ((C1+C3) - (C2+C4))/A CP8 = ((C1+C4) - (C2+C3))/A 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 tyres. 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 tyres. 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 tyre 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 tyre from the set up procedure selected randomly. After completion of training the reliability of the neural network in predicting tyre pressures was assessed using the whole of the stored data relating to one deflated tyre. 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 tyre and the predicted tyre 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 tyre having a reduced inflation pressure of 1.6 bar whilst the remainder relate to a normally inflated tyre at a pressure of 2.4 bar. The reliability of the trained neural network to distinguish between the normally inflated tyre and the underinflated tyre is illustrated in Table 2 which shows the separation matrix of predicted tyre pressures. For the purpose of ranking in Table 2, the predicted tyre 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 tyres the network predicted that 102 data sets related to tyres at 1.6-1.8 bar, 5 sets related to tyres at 1.8-1.9 bar and one set related to a tyre at 1.9-2.1 bar. Similarly for the 108 sets of data from normally inflated tyres, the network predicted that 99 sets related to tyres at above 2.2 bar, 2 sets related to tyres at 2.1-2.2 bar, 4 sets related to tyres at 1.9-2.1 bar and 3 sets related to tyres 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 tyre pressure from the corresponding eight normal driving parameters derived from the four wheel speed signal pulse counts C1-C4 monitored in normal driving. The tyre pressures of the wheels were then displayed on a digital readout. In a second embodiment the predicted tyre pressures were compared with the scheduled tyre pressures for the vehicle and in the event that a tyre was found to be deflated then an alarm was operated to warn the driver that a tyre 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. Whilst 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 tyres. 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 whilst the above-described embodiment utilises the eight parameters CP1-8 both in training the neural network and subsequently in applying the trained network to the monitoring of the tyres 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 tyre 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 a third embodiment 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 "speed" 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 minimising the training time and maximising the accuracy of the trained network in the application of the method to different types of vehicle.

Claims: A method of determining the inflation pressure of one or more pneumatic tires on a moving vehicle having a plurality of wheels (n) fitted with tires comprising the steps of: performing a set-up procedure including, for a range of tire inflation pressures including all the vehicles tires at their scheduled inflation pressure and combinations of one or more tires at a range of pressures below their scheduled pressure and for a range of vehicle speeds and driving conditions to include accelerating, braking, straight-ahead driving and cornering, the steps of: deriving for each of the wheels (n) a set-up speed value (Cn) proportional to the wheel angular velocity, determining the relationship of the set of set-up speed values (Cn) to the set of tire pressures by means of teaching a trainable neural network information processing system having an input layer of neurodes and an output layer of neurodes to map the set-up speed values onto the set of tire pressures by deriving from each set of set-up speed values (Cn) intermediary speed parameters (CP1-CP8) each derived from at least two of the set of set-up speed values (Cn) and inputting each intermediary speed parameter (CP1-CP8) to a different neurode of the input layer of neurodes and taking the inflation pressure of each of the tires from a different neurode of the output layer of neurodes and storing in a data storage means the determined relationship of the set of set-up speed values (Cn) to each related tire pressure, and subsequently in normal driving monitoring, in subsequent normal driving, 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, and obtaining the associated tire inflation pressures by using the determined relationship stored in the data storage means. A method according to claim 1, characterised in that the intermediary speed parameters (CP1-CP8) comprise an average speed parameter (A) which is calculated as the sum of each of the wheel speed values (Cn) divided by the number of wheels n and parameters related to the speed of each wheel n. A method according to claim 1 or 2, characterised in that the intermediary speed parameters (CP1-CP8) comprise a parameter relating to the difference in the sum of wheel speed values of wheels positioned at the front of the vehicle and those at the rear of the vehicle. A method according to any of claims 1 to 3, characterised in that the intermediary speed parameters (CP1-CP8) comprise a parameter relating to the difference in the sum of wheel speed values (Cn) of wheels on the left side of the vehicle and those wheels on the right side of the vehicle. A method according to any of claims 1 to 4, characterised in that the intermediary speed parameters (CP1-CP8) comprise a parameter related to the difference in the sums of wheel speed values (Cn) of diagonally opposed groups of wheels. A method according to claim 5, characterised in that the vehicle has four wheels (W1-W4) and the speed parameters comprise eight speed parameters (CP1-CP8) wherein CP1 = Average speed parameter A = (C1+C2+C3+C4)/4 CP2 = 1 - (C1/A) CP3 = 1 - (C2/A) CP4 = 1 - (C3/A) CP5 = 1 - (C4/A) CP6 = ((C1 + C2) - (C3 + C4))/A CP7 = ((C1 + C3) - (C2 + C4))/A CP8 = ((C1 + C4) - (C2 + C3))/A wherein C1 - C4 are the respective wheel speed values of left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle. A method according to any of claims 1 to 5, characterised in that the vehicle has four wheels. A method according to any of claims 1 to 5, characterised in that the vehicle has two wheels. A method according to claim 8, characterised in that a further signal determined by the suspension deflection is used as a "speed" value. A method according to any of claims 1 to 9, characterised in that the wheel speed values Cn are calculated as the number of signal counts or pulses in a predetermined interval of time from each of the wheel signal generators of an electronic anti-lock braking system. A method according to claim 10, characterised in that the wheel signal generators produce 96 pulse per revolution of wheel. A method according to claim 10 or 11, characterised in that the interval of time is 5 seconds. A method according to any of claims 1 to 12, characterised in that the trained information processing system is carried out by means of a neurocomputer. A method according to any of claims 1 to 13, characterised in that the neural network comprises additional plural hidden layers of neurodes. A method according to claim 14, characterised in that the neural network is a Multi-Layer Perceptron Network. A method according to claim 15, characterised in that the Multi-Layer Perceptron Network comprises an input layer of neurodes, an output layer of neurodes and two hidden layers of neurodes. A method of detecting a deflated tyre or tyres on a vehicle comprising obtaining values for the tyre inflation pressures according to any of claims 1 to 16, then comparing each of the identified tyre inflation pressures with the required scheduled inflation pressure of that tyre and when one or more of the stored tyre inflation pressures is lower than the required inflation pressure operating an alarm to warn that one or more tyres of the vehicle are deflated.

Inventors: REHAL LAVINDER SINGH, STEPHENS PAUL, WALKER JOHN CHARLES, REHAL, LAVINDER SINGH, STEPHENS, PAUL, WALKER, JOHN CHARLES
Applicants: SUMITOMO RUBBER IND, SUMITOMO RUBBER INDUSTRIES LIMITED
Application year: 1996
Main classification: B60C23
IPC codes: B60C23, B60T8