Simultaneous state and fault estimation for linear stochastic systems

Gannouni, F.; Hmida, Fayçal Ben

doi:10.1109/sta.2017.8314965

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…According to the literature, researchers classify the adaptive filters into four types: Bayesian-based, maximum likelihood-based, correlationbased, and covariance matching techniques (Tripathi et al, 2016). In addition, the augmented Kalman filter is often used to estimate the system states and fault and disturbance signals simultaneously (Gannouni & Hmida, 2017). Various applications of Kalman filter-based fault detection system can be found in the gas turbine engine (Pourbabaee et al 2016), synchronous generator (Nadarajan et al, 2016), power systems (Liu & He, 2017), wind turbine system (Cho et al, 2018), and aircraft (Marzat et al, 2012).…”

Section: Kalman Filter-based Fault Detectionmentioning

confidence: 99%

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: 99%

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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“…To get the proper data, the gyroscope data should be centred to zero using the bias value. The matrices A and B are defined in (8) through (11).…”

Section: Theory Of Kalman and Complementary Filters 21 Kalman Filtermentioning

confidence: 99%

“…θ˙k value is output from the gyroscope and Δt is the sampling time. From (7), the vector x → k is defined in (11). 1 illustrates the Kalman filter, where the input is the roll and itch from Accelerometer sensor, and angular velocities of roll and pitch of the gyroscope sensor.…”

Section: Theory Of Kalman and Complementary Filters 21 Kalman Filtermentioning

confidence: 99%

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Experimental study of a novel filter structure designed for MEMS‐based sensors in electric vehicles

Linani

Mokhtari

Cheknane

et al. 2019

IET Power Electronics

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This work describes a comparative study between Kalman filter, a complementary filter and a combination of both, for use in electrical vehicles. Combining the benefits offered by each filter to obtain an optimised filter combination is targeted. Three different combinations: The Kalman-complementary filter (KCF), complementary-Kalman filter (CKF) and 2KCFs are examined here. The filters are used to improve signals obtained via two sensors (gyroscope and accelerometer) integrated into the sensor IMU-MPU6050, with internal DMP. The sensor data are filtered to guarantee the movement quality of electrical vehicles. The KCF combination shows higher performance than the CKF combination. Moreover, the experimental results show that the 2KCF combination yields best performance with minimal noise levels and more accurate angle measurement. The optimal combination is strongly recommended for future electrical vehicle development.

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Simultaneous state and fault estimation for linear stochastic systems

Cited by 2 publications

References 12 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

Experimental study of a novel filter structure designed for MEMS‐based sensors in electric vehicles

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