Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Simani, Silvio; Spina, Pier Ruggero

doi:10.1142/9789814447317_0074

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…However, the alarm threshold is bound to rise to reduce FAR due to the measurement noise. Considering that our fault estimation indicator is system state, not the system output, an additional calculation process to obtain the estimated system output is performed in order to be consistent with the method of [15]. Similarly, one estimated system output with fault information and four ones without fault information are provided.…”

Section: Robustness Analysis Experimentsmentioning

confidence: 99%

“…A comparative experiment for sensor n l drifting fault in 80 s between our proposed fault estimation scheme and that proposed by [15] is implemented. The indicator of fault estimation scheme in [15] is the difference between the estimated output and the actual output. However, the alarm threshold is bound to rise to reduce FAR due to the measurement noise.…”

Section: Robustness Analysis Experimentsmentioning

confidence: 99%

“…Nowadays, KF-based sensor FDI schemes have been successfully designed and tested for GTs. Simani and Spina [15] proposed a sensor FDII strategy based on a linear dynamic system and a bank of KFs in which a set of measured variables of the system is compared with the corresponding signals estimated by filters to generate residual functions that can be used for sensor fault isolation. In the literature [16], an aircraft engine sensor fault diagnostics method with an enhanced bank of KFs is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines

Zhu

Liu

et al. 2020

Energies

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In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a coupling diagnosis method with the integration of the model-based method and unsupervised learning algorithm is proposed and developed for monitoring gas turbine sensor faults, which represents an integration of Square Root Cubature Kalman Filters (SRCKF) and an improved Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. A detection indicator produced by SRCKF with a specific hypothesis is used for extracting sensor fault features against process and measurement noise, as well as operating conditions. Then, an improved DBSCAN is implemented based on a voting scheme to detect and isolate the faulty sensors. Finally, a residual-based fault estimation scheme is proposed to track sensor fault evolution and help to judge the types of faults. Moreover, the observability of the model involved is analyzed to verify the stable operation of the FDI&E scheme. Various experiments for single and concurrent sensor fault scenarios in a dual-spool gas turbine prototype during a whole flight mission are conducted to demonstrate the effectiveness of the proposed FDI&E scheme. Moreover, comparative studies confirm the superiority of our proposed FDI&E scheme than the existing methods in terms of promptness and robustness of the sensor FDI.

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Section: Robustness Analysis Experimentsmentioning

confidence: 99%

Section: Robustness Analysis Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines

Zhu

Liu

et al. 2020

Energies

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“…5, since, as mentioned above, if the number of zeroes per column of the fault matrix increases, the residual standard deviations also increase. Another method to reduce the minimal faults to be isolated may be the use of statistical tests on the residual whiteness, instead of comparing the residual amounts with fixed thresholds (geometrical analysis of residuals) (Simani and Spina, 1998).…”

Section: Reduction Of the Minimal Sensor Fault To Be Isolatedmentioning

confidence: 99%

A Method for the Diagnosis of Gas Turbine Sensor Faults in Presence of Measurement Noise

Bettocchi

Spina

1999

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

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This paper presents a method for the detection and isolation of single gas turbine sensor faults, in presence of model inaccuracy and measurement noise. The method uses a fault matrix with a column-canonical structure (i.e., each matrix column having the same number of zeroes, but in different positions), in order to obtain the unambiguous fault isolation. The fault matrix was obtained by using a number of ARX (Auto Regressive exogenous) MISO (Multi-Input/Single-Output) models equal to the number of measured gas turbine outputs, each model calculating an estimate of one measurable output as a function of other inputs or outputs measured on the machine. Moreover, in order to reduce the threshold of fault detection and, therefore, the minimal detectable faults, digital filters were used, applied to the time series of data measured on the machine and computed by the models. Finally, tests were performed in order to find the minimal sensor faults that can be detected and isolated.

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“…The objective of fault isolation is to determine if a fault has occurred and also the location of the fault, by analysing the residual vector. The problem of detecting and isolating faults to an industrial gas turbine was studied in other previous papers in the literature Patton et al 2000;Patton and Simani, 1999;Simani and Spina, 1998) using mainly observer based techniques. In this paper we investigate the problem of fault diagnosis of an industrial gas turbine using a neurofuzzy approach.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of an Industrial Gas Turbine Using Neuro-Fuzzy Methods

Palade

Patton

Uppal

et al. 2002

IFAC Proceedings Volumes

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The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The paper is concerned on how to obtain an interpretable fault classifier as well as interpretable models for residual generation. Copyright © 2002 IFAC

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Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Cited by 13 publications

References 8 publications

A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines

A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines

A Method for the Diagnosis of Gas Turbine Sensor Faults in Presence of Measurement Noise

Fault Diagnosis of an Industrial Gas Turbine Using Neuro-Fuzzy Methods

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