A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

Kim, Pyung Soo

doi:10.1515/msr-2017-0010

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…As shown in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], the state-space approach has been a general method for modeling, analyzing and designing a wide range of control and estimation problems in diverse dynamic systems, and has been especially suitable for digital computation techniques. Therefore, in this paper, a general discrete-time state space model with noises is considered as follows:…”

Section: Finite Memory Structure Filter From the Conditional Density mentioning

confidence: 99%

“…In particular, the electric motor powered by direct current source is the most widely and successfully adopted in motor control systems due to the intrinsic good properties such as efficient cost, high performance, etc. [20,27]. The discrete-time nominal direct current electric motor model without model uncertainty is given as follows:…”

Section: F404 Gas Turbine Aircraft Engine and Electric Motor Systemsmentioning

confidence: 99%

“…Hence, although two FMS filtersx p i (13) andx s i (14) are designed for the nominal state space models (17) and (18) with A and C, they are applied actually for the temporarily uncertain system (19) with model uncertainties (20) and 21, respectively.…”

Section: Model Uncertaintiesmentioning

confidence: 99%

“…Meanwhile, in contrast to the recursive infinite memory structure (IMS) filter like the traditionally used Kalman filter [9][10][11][12], the finite memory structure (FMS) filter has been known to have inherent good properties such as bounded-input, bounded-output (BIBO) stability and and more robustness against temporary uncertainties due to its processing manner of finite measurements on the most recent window [13][14][15][16]. Thus, the FMS filter has been applied successfully for various engineering problems [17][18][19][20][21][22][23]. As shown in [13][14][15][16], the FMS filter is known to have better noise suppression as the measurement window length grows.…”

Section: Introductionmentioning

confidence: 99%

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Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Kim

2019

Applied Sciences

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In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.

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Section: Finite Memory Structure Filter From the Conditional Density mentioning

confidence: 99%

Section: F404 Gas Turbine Aircraft Engine and Electric Motor Systemsmentioning

confidence: 99%

Section: Model Uncertaintiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Kim

2019

Applied Sciences

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“…Therefore, as an alternative to the Kalman filter, the finite memory structure (FMS) filter has been designed for state estimation and shown inherently to have BIBO stability and be more robust against temporary uncertainties [6][7][8][9]. This FMS filter has been applied successfully in various engineering fields [10][11][12]. Likewise the recursive Kalman filter in [1][2][3][4][5], the FMS filter in [6][7][8][9] is a causal filter that provides state estimates at given times based only on the relative past.…”

Section: Introductionmentioning

confidence: 99%

Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments

Kim

2019

Applied Sciences

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In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.

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Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

Chen

et al. 2022

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Purpose This paper aims to provide a novel integrated fault detection method for industrial process monitoring. Design/methodology/approach A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output. Findings The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process. Originality/value The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

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A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

Cited by 3 publications

References 16 publications

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

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