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
Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is ine...Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.展开更多
Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable a...Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable aerodynamic forces,lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade.It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine.In this paper,a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades.The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer.The statistical,histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.展开更多
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Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.
文摘Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable aerodynamic forces,lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade.It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine.In this paper,a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades.The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer.The statistical,histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.
