According to the shearing force character and the deformation coordination condition of shell at the station of supports, the mathematical models to calculate contact angle and contact pressure distribution between ty...According to the shearing force character and the deformation coordination condition of shell at the station of supports, the mathematical models to calculate contact angle and contact pressure distribution between tyre and shell were set up, the formulae of bending moment and bending stress of tyre were obtained. Taking the maximum of tyre fatigue life as the optimal objective, the optimization model of tyre support angle was built. The computational results show that when tyre support angle is 30°, tyre life is far less than that when tyre support angle is optimal, which is 35.6°, and it is unsuitable to stipulate tyre support angle to be 30° in traditional design. The larger the load, the less the nominal stress amplitude increment of tyre, the more favorable the tyre fatigue life when tyre support angle is optimal.展开更多
Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A diffe...Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.展开更多
The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The exi...The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The existing instru-mental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires reg-ular manual conduct.Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition.In this perspective,the Machine Learning(ML)approach acts appropriate as it exhi-bits comparison of specific performance in the past with present,intended for predicting the same in near future.The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle.In order to examine the vibration response of a wheel hub,the in-house design&development of DAQ(Data Acquisition System)is described.Micro Electro-Mechanical Scheme(MEMS)built accelerometer is incorporated with open source hardware and software to collect and store the data.This framework is easy to develop,monitor and can be retrofitted in two wheeled vehicle.For various pressure conditions,the change in response of wheel hub vibration with respect to time is collected.The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters.The classification of different conditions of tyre pressure is carried out using ML classifiers.展开更多
Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many me...Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.展开更多
According to the character and the condition for shell deformation and force balance, we have set up the mathematical models of the contact angle and the contact pressure distribution between the tyre and the shell, s...According to the character and the condition for shell deformation and force balance, we have set up the mathematical models of the contact angle and the contact pressure distribution between the tyre and the shell, studied The features of a multi-body contact model between the roller and the tyre, and developed the build-up procedure of the contact finite element model between the roller and the tyre. The stress distribution rule of the roller and the tyre is obtained as follows. The contact center is the center of the contact stress circle, and the contact stress is also reduced gradually from the contact center to the contact boundary. The equivalent stress of the tyre varies 5 times per circle, thus making the trye fracture easily.展开更多
基金Project(2005038227) supported by the China Postdoctoral Science Foundation project(04JJ3050) supported by the Hu-nan Natural Science Foundation
文摘According to the shearing force character and the deformation coordination condition of shell at the station of supports, the mathematical models to calculate contact angle and contact pressure distribution between tyre and shell were set up, the formulae of bending moment and bending stress of tyre were obtained. Taking the maximum of tyre fatigue life as the optimal objective, the optimization model of tyre support angle was built. The computational results show that when tyre support angle is 30°, tyre life is far less than that when tyre support angle is optimal, which is 35.6°, and it is unsuitable to stipulate tyre support angle to be 30° in traditional design. The larger the load, the less the nominal stress amplitude increment of tyre, the more favorable the tyre fatigue life when tyre support angle is optimal.
文摘Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.
文摘The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The existing instru-mental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires reg-ular manual conduct.Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition.In this perspective,the Machine Learning(ML)approach acts appropriate as it exhi-bits comparison of specific performance in the past with present,intended for predicting the same in near future.The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle.In order to examine the vibration response of a wheel hub,the in-house design&development of DAQ(Data Acquisition System)is described.Micro Electro-Mechanical Scheme(MEMS)built accelerometer is incorporated with open source hardware and software to collect and store the data.This framework is easy to develop,monitor and can be retrofitted in two wheeled vehicle.For various pressure conditions,the change in response of wheel hub vibration with respect to time is collected.The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters.The classification of different conditions of tyre pressure is carried out using ML classifiers.
文摘Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.
基金This workis supported by Science and Technology Planin Hunan Education Department Project No.(06C758)
文摘According to the character and the condition for shell deformation and force balance, we have set up the mathematical models of the contact angle and the contact pressure distribution between the tyre and the shell, studied The features of a multi-body contact model between the roller and the tyre, and developed the build-up procedure of the contact finite element model between the roller and the tyre. The stress distribution rule of the roller and the tyre is obtained as follows. The contact center is the center of the contact stress circle, and the contact stress is also reduced gradually from the contact center to the contact boundary. The equivalent stress of the tyre varies 5 times per circle, thus making the trye fracture easily.