Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles

Safavi, Saeid; Safavi, Mohammad Amin; Hamid, Hossein; Fallah, Saber

doi:10.3390/s21072547

Cited by 58 publications

(21 citation statements)

References 30 publications

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“…e comparative study was conducted between autonomous vehicles and human drivers under a simulated environment for a mixed traffic scenario. Safavi et al [122] addressed autonomous vehicle health forecasting using the Internet of ings and artificial intelligence. e sensors are the critical part of the intelligent system of autonomous vehicles; however, these sensors may fail to function properly due to various dynamic factors.…”

Section: Advanced Technologies and Autonomous Vehiclesmentioning

confidence: 99%

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bathla

Bhadane²,

Singh

et al. 2022

Mobile Information Systems

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Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

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Section: Advanced Technologies and Autonomous Vehiclesmentioning

confidence: 99%

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bathla

Bhadane²,

Singh

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…It is also worth mentioning at this point that sensors also introduce various types of imperfections into the whole system, such as measurement noise or offsets, among others. This issue has been addressed in several papers [ 14 , 15 , 16 ]. It is important that the developed control algorithm, in spite of the existing imperfections, is able to keep the vehicle in the assumed trajectory and perform the requested manoeuvre.…”

Section: Introductionmentioning

confidence: 99%

The Validity of Sensors and Model in the Lane Change Control Process

Dębowski

Faryński

Żardecki

2023

Sensors

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The paper demonstrates the validity of sensors and the model in the algorithm for a lane change controller. The paper presents the systematic derivation of the chosen model from the ground up and the important role played by the sensors used in this system. The whole concept of the system on which the tests were carried out is presented step by step. Simulations were realised in the Matlab and Simulink environments. Preliminary tests were performed to confirm the need for the controller in a closed-loop system. On the other hand, sensitivity (the influence of noise and offset) studies showed the advantages and disadvantages of the developed algorithm. This allowed us to create a research path for future work with the aim of improving the operation of the proposed system.

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“…[4]. However, due to noise contribution from multiple sources, accurate machine fault detection using these signal traits is a challenging task [5].…”

Section: Introductionmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles

Cited by 58 publications

References 30 publications

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

The Validity of Sensors and Model in the Lane Change Control Process

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

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