Robust Fault Diagnosis of Sensor Faults in Power Converter used in Hybrid Electric Vehicle

Kleilat, Iman; Al-Sheikh, Hiba; Moubayed, Nazih; Hoblos, Ghaleb

doi:10.1016/j.ifacol.2018.09.597

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…The unified approach is presented in Doraiswami and Cheded (2013) to detect and isolate the fault in a linear discrete-time system with measurement and system noise using the Kalman filter. Switched Kalman filter is designed for sensor fault detection and isolation in power converters (Kleilat et al, 2018). The proposed filter is an extended version of the standard Kalman filter combined with a disturbance decoupling observer and has confirmed satisfactory results disturbance.…”

Section: Kalman Filter-based Fault Detectionmentioning

confidence: 94%

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

Ahmad¹,

Mohd-Mokhta²

2021

JST

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With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.

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Section: Kalman Filter-based Fault Detectionmentioning

confidence: 94%

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

Ahmad¹,

Mohd-Mokhta²

2021

JST

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“…In this study, the method of artificial intelligence is used to determine the feature vectors of physiological information and text information. For the classification and judgment of correlation, the random forest algorithm is used [ 25 ]. When the recognition accuracy of the model meets the threshold, it means that the model parameters have been trained and can be used to verify the test sample data.…”

Section: Artificial Intelligence Correlation Analysis Modelmentioning

confidence: 99%

Correlation Analysis between Higher Education Level and College Students’ Public Mental Health Driven by AI

Yin-ying

Tang

2022

Computational Intelligence and Neuroscience

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Generally, there is a certain correlation between the level of higher education and the public mental health of college students. Traditionally, questionnaires and literature research methods are used to analyze the correlation between mental health and higher education, but these methods are always limited by many factors, such as resource conditions, survey paths, theoretical framework, and technical means. In recent years, with the rapid development and application of artificial intelligence technology, a new direction of analyzing the correlation between higher education level and college students’ public mental health has been given. The artificial intelligence method makes the correlation analysis change from subjective to big data algorithm evaluation, which can make up for the shortcomings and inefficiency of traditional methods, truly analyze the degree of correlation, and put forward exact solutions, which is of great significance for further evaluating and monitoring the public mental health of college students in higher education. This study first analyzes different AI algorithms and determines to use convolution neural network and random forest algorithm to establish an AI correlation model. After testing and data analysis, the established model has an accuracy of 87.5% in the determination and analysis of correlation. Compared with support vector machine (SVM) and backpropagation (BP) neural network algorithm, it has a higher recognition accuracy.

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“…Therefore, in the very near future, there will be urgent issues of maintaining and restoring their performance, providing high-quality diagnostics. There are known studies aimed at studying the diagnostics of battery system failures in electric and hybrid cars [1][2][3][4][5][6][7]. However, it is necessary to study the traction and speed properties of such vehicles.…”

Section: Introductionmentioning

confidence: 99%

Traction control and diagnostics of electric and unmanned vehicles on roller stands

2020

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The authors of the article analyze the possibilities of correct diagnostics of unmanned and electric vehicles on modern power and inertia roller stands. The purpose of this work is to improve the quality of monitoring the technical condition of wheeled vehicles by providing highly informative and stable test modes of operation in the process of their diagnostics. To achieve this target, we have developed test methods for wheeled vehicles, the implementation of which it is proposed to use a hybrid designed stand allows to measure force and power to the drive wheels of the vehicle, and temporal and kinematic parameters of the process of its functioning. The study proved that, to ensure a highly stable and test modes of operation of wheeled vehicles in the process of control of technical condition and diagnosis, you should use hybrid stands.

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Robust Fault Diagnosis of Sensor Faults in Power Converter used in Hybrid Electric Vehicle

Cited by 10 publications

References 3 publications

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

Correlation Analysis between Higher Education Level and College Students’ Public Mental Health Driven by AI

Traction control and diagnostics of electric and unmanned vehicles on roller stands

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