Design of Tire Pressure Monitoring System Using a Pressure Sensor Base

Silalahi, Lukman Medriavin; Alaydrus, Mudrik; Rochendi, Agus Dendi; Muhtar, Muhtar

doi:10.22441/sinergi.2019.1.010

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…Recent research has provided insightful information building on the ground-breaking work of Hasan et al, which included pressure sensors, signal conditioning, radio frequency transmitters, and batteries [5]. Silalahi et al built a TCMS incorporating pressure sensors, microcontrollers, and bluetooth connectivity [6]. For vehicle suspension, Lee et al investigated an indirect TCMS employing adaptive extended Kalman fltering [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Arora,

Naveen Venkatesh,

Sugumaran

et al. 2024

Journal of Engineering

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Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

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Section: Introductionmentioning

confidence: 99%

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Arora,

Naveen Venkatesh,

Sugumaran

et al. 2024

Journal of Engineering

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“…Maintaining the appropriate tyre pressure is crucial for preventing accidents and optimizing vehicle performance. TPMS systems provide real-time monitoring of tyre pressure and temperature, allowing drivers to detect and address any issues before they become a major safety concern [2]. Furthermore, TPMS in vehicles, heavy vehicles, and trailers also offer several benefits.…”

Section: Introductionmentioning

confidence: 99%

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

Kulkundri,

Desai,

Uday Kalkundri

2024

IJECE

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This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

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“…Development in the field of TPMS is gradually gaining momentum. In 2017, Silalahi et al examined the design of a TPMS using a pressure sensor, microcontroller and Bluetooth communication protocol [ 1 ]. This was based on the original approach adopted by Hasan et al, encompassing a pressure sensor, signal conditioning unit, switch, RF transmitter microcontroller and long-life battery [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Vasan

Sridharan

Sreelatha

et al. 2023

Sensors

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Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

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Design of Tire Pressure Monitoring System Using a Pressure Sensor Base

Cited by 17 publications

References 13 publications

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

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