Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach

Vanini, Z. N. Sadough; Khorasani, K.; Meskin, Nader

doi:10.1016/j.ins.2013.05.032

Cited by 135 publications

(24 citation statements)

References 14 publications

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“…ă N th (6) Once N th is close to value N eff,k , all particles have almost the same significance. The architecture of a conventional PF mentioned above is that the measured data from different sensors are sent to one central PF to process together, and this is so-called the central architecture [15,17].…”

Section: The Particle Filtermentioning

confidence: 88%

“…The engine performance tracking problem can be regarded as calculating the health parameters. Various methods such as Kalman filters (KFs), neural networks, fuzzy logic, genetic algorithms and expert systems have been proposed to obtain health parameters for engine health monitoring [3][4][5][6][7]. KF-based methods seem to be the most common ones for gas turbine health estimation, but these techniques are mainly focused on engine steady state, e.g., under cruise or average conditions [8].…”

Section: Introductionmentioning

confidence: 99%

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Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

et al. 2015

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This paper considers the problem of gas turbine transient performance tracking in a cluttered environment. To increase the accuracy and robustness of state estimation, a data-fusion nonlinear estimation method based on an adaptive particle filter (PF) is proposed. This method needs local estimates transmitted to a central filtering unit for data fusion, and then global data feedback to the local PF for consensus propagation. The computational burden is shared by the local PF and central filtering unit in the data-fusion architecture. Furthermore, the PF algorithm used for the data fusion is embedded with the prior knowledge of engine health condition and adaptive to the measurement noise, and hence is called the adaptive PF. The heuristic information of state variables represented by inequality constraints tunes the local estimates by a probability density truncation method. The covariance of measurement noise is calculated by wavelet transform and utilized to update the particle importance function of the real time PF. The performance improvements of the proposed method are indicated through extensive experiments for gradual and abrupt shift performance tracking under conditions of gas turbine transient operation.

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Section: The Particle Filtermentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

et al. 2015

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“…Khorasani et al proposed a fault diagnosis and separation program based on a neural network. This program was designed for manual injection engine faults with high dynamic nonlinearity and was found to exhibit good fault diagnosis performance [3][4]. To diagnose flameout faults in diesel engines, Liu et al [5] suggested to reduce the dimension of effective vibration signals with rough set and then established a back propagation (BP) neural network model to identify vibration signal patterns.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

2016

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Abstract-to identify the hang, collision and drift faults of methane sensors, this paper presents a fault diagnosis method for methane sensors using multi-sensor information fusion. A methane concentration monitoring approximation model with multi-sensor information fusion is established based on generalized regression neural network (GRNN). The output of the neural network is compared with the measured value of the sensor to be diagnosed to obtain the variation curve of the residual error signal. Through the analysis of the variation tendency of the residual error signal, the fault status of a methane sensor could be determined based on a reasonable threshold. Through simulation comparison is applied between the two models of GRNN and BP neural network; verify the GRNN model is much more precise in the approximation of methane concentrations. Fault diagnosis for methane sensors using generalized regression neural network is effective and more efficient.Index Terms-generalized regression neural network (GRNN), methane sensor, fault diagnosis, multi-sensor information fusion

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“…Diagnostic expert systems are a natural candidate for extracting diagnostic information from complex data, and have been applied in the diagnostics of turbojet engines [49,50]. The aim of the present study is to create a specialized approach for the creation of a rule-base of an expert system which can transform class information in an infrared image into useful diagnostic information, thus creating hybrid architecture.The functionality of this approach has been evaluated and designed specifically for application to turbojet engines, and the approach can be considered as novel compared to other intelligent approaches in turbojet engine diagnostics, which are applied to data measured by the engines' sensors and do not deal with visual/infrared image information [51][52][53]. The aim of this paper is to explore the design of this new approach and prove its functionality by applying thermal vision-based diagnostics in an automated adaptive system in the area of turbojet engine diagnostics.…”

mentioning

confidence: 99%

“…The functionality of this approach has been evaluated and designed specifically for application to turbojet engines, and the approach can be considered as novel compared to other intelligent approaches in turbojet engine diagnostics, which are applied to data measured by the engines' sensors and do not deal with visual/infrared image information [51][52][53]. The aim of this paper is to explore the design of this new approach and prove its functionality by applying thermal vision-based diagnostics in an automated adaptive system in the area of turbojet engine diagnostics.…”

mentioning

confidence: 99%

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

et al. 2019

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There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

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Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach

Cited by 135 publications

References 14 publications

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

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