Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems

Jeong, Kicheol; Choi, Seibum B.; Choi, Hun-Kook

doi:10.1109/tvt.2020.2977353

Cited by 32 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is mostly used to address categorization issues. Each feature in the SVM method is represented by the value of a particular coordinate, and each feature in n-dimensional space is represented as a point (where n is the number of available features) [23]. Then, categorization is achieved by identifying the hyper-plane that effectively separates the two groups [24].…”

Section:  Information Gainmentioning

confidence: 99%

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Mahdi¹,

Gittaffa²,

Issa³

2022

JESA

View full text Add to dashboard Cite

The most significant and sensitive component of the biomedical field is the care of newborn newborns. Because of their gestational age or birth weight, certain newborn newborns are at a higher risk of death. Because of their unmet demand for warmth, the majority of preterm newborns born between 32 and 37 weeks of gestation die. The neonatal incubator is a device that provides a regulated and closed environment to preterm newborns. In this paper and depending on technological progress, an intelligent system has been designed to monitor the performance of the incubator sensors depending on four features (temperature, humidity, fan current, and heater current) to detect any fault in the system. The intelligent system employs a low-power computing device to detect the fault in sensors, like the Raspberry Pi 4, which delivers the data from the incubator’s sensors. For classification, tasks adopted many algorithms like Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), then send alerting messages (e-mail, text message) to the nurse or doctor via Wi-Fi. The promising results of the proposed method accuracy come as 98% of DT and SVM while 97.3% of NN.

show abstract

Section:  Information Gainmentioning

confidence: 99%

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Mahdi¹,

Gittaffa²,

Issa³

2022

JESA

View full text Add to dashboard Cite

show abstract

“…When we rewrite the update rule (29) into the derivative of the Lyapunov equation (28) turns into (30).…”

Section: = −̂ (25)mentioning

confidence: 99%

“…Three different main dynamic models are used in suspension control systems. These are quarter-car vehicle [28]- [30], half-car vehicle [31]- [33], and full-car vehicle models [34]- [37]. In general, the left and the right sides of the vehicles are symmetrical, so the mathematical model of the vehicle can be customizable as a half-car model.…”

Section: Introductionmentioning

confidence: 99%

Vibration Control of Truck Cabins With the Adaptive Vectorial Backstepping Design of Electromagnetic Active Suspension System

Başaran

2020

IEEE Access

View full text Add to dashboard Cite

The active suspension system is an important equipment that isolates the vibrations that may come from outside in a land vehicle. In heavy vehicles, the active suspension system can be used to dampen vibrations in the driver's cabin. The electromagnetic actuator is used as an active element in suspension system. Traditionally, the use of active suspension systems is mainly on automobiles and the studies related to heavy vehicles like trucks lack enough interest. In this study, dynamic modeling of a three-axle heavy vehicle cabin is performed with a half-car approach and it is aimed to suppress the disruptive effects coming from the road with active electromagnetic actuators. Lyapunov based backstepping control design is expressed in vectorial form for the heavy vehicle system, which is a multi-input multi-output system. To demonstrate the performance of the designed controller, a comparison has been made for the active and passive states for the truck cabin suspension system. The main feature of the applied control method is that it does not require knowing the actuator parameters, the adaptive term can handle the estimate of the actuator parameter. The stability of the proposed system has been proven via Lyapunov based arguments and given simulation works. The results obtained are important to suppress vibration, consequently, decreases the vibration exposure for truck drivers in terms of occupational health and safety aspects. INDEX TERMS Adaptive backstepping control, Truck cabin suspension system, Truck modeling.

show abstract

“…Note that all mentioned studies focused on error compensation for actuator defects. Actually, sensor failure is more serious than actuator because the system performance is highly dependent on the sensor signal for feedback control design [46]. Nevertheless, most prior suspension studies assumed that all communication channels from the sensors were perfect.…”

Section: Introductionmentioning

confidence: 99%

Observer Based Adaptive Neural Networks Fault-Tolerant Control for Pneumatic Active Suspension With Vertical Constraint and Sensor Fault

Ahn

2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

This paper treats the problem of a pneumatic active suspension considering uncertain parameters and displacement constraints under the presence of sensor failure and unmeasured states. A quarter car model is established using an air spring to provide flexible stiffness and an active force to suppress the chassis vibrations. To approximate unknown nonlinear functions of pneumatic actuator dynamic, neural networks are employed as a function approximator. The sensor fault is investigated while all system states of the controlled suspension are assumed to be unmeasurable. Then, a neural state observer is constructed to estimate unmeasured states and compensate for the partial loss of effectiveness of sensor failure simultaneously. An adaptive fault tolerant control is proposed via the command filter backstepping technique to alleviate the effects of sensor failure and solve the explosion of complexity problem. In addition, to improve the tracking accuracy and get the ride comfort, this study is concerned with a prescribed performance function so that the vertical displacement of sprung mass does not violate the predefined boundary. The Lyapunov theorem is then applied to demonstrate that all system signals are semi-globally uniformly ultimately bounded while error variables can converge to the small neighborhood of zero. Finally, comparative simulation examples are analyzed to verify the effectiveness of the designed control with sensor noise and unmeasurable variables.

show abstract

Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems

Cited by 32 publications

References 34 publications

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Vibration Control of Truck Cabins With the Adaptive Vectorial Backstepping Design of Electromagnetic Active Suspension System

Observer Based Adaptive Neural Networks Fault-Tolerant Control for Pneumatic Active Suspension With Vertical Constraint and Sensor Fault

Contact Info

Product

Resources

About