Model-based Fault Detection and Isolation Using Neural Networks: An Industrial Gas Turbine Case Study

2011 ◽  
Author(s):  
Hasan Abbasi Nozari ◽  
Hamed Dehghan Banadaki ◽  
Mehdi Aliyari Shoorehdeli ◽  
Silvio Simani
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Industrial Gas Turbine

scholarly journals Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 337
Author(s):  
Amare Desalegn Fentaye ◽  
Valentina Zaccaria ◽  
Konstantinos Kyprianidis
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Gas Turbine ◽  
Monitoring System ◽  
Convolutional Neural Networks ◽  
Fault Detection And Isolation ◽  
Fault Classification ◽  
Isolation Method ◽  
Trend Monitoring ◽  
Modular Architectures

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

scholarly journals A CASE STUDY OF VIBRATION FAULT DIAGNOSIS APPLIED AT ROLLS-ROYCE T-56 TURBOPROP ENGINE

Aviation ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 78-82
Author(s):  
Christos Skliros
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Root Cause ◽  
Significant Challenge ◽  
Turboprop Engine

Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.

scholarly journals Fault Detection and Isolation Based on Dynamic Observers Applied to Gas Turbine Control Sensors

1998 ◽  
Author(s):  
S. Simani ◽  
P. R. Spina ◽  
S. Beghelli ◽  
R. Bettocchi ◽  
C. Fantuzzi
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Actual System ◽  
Analytical Redundancy ◽  
Residual Functions ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In order to prevent machine malfunctions and to determine the machine operating state, it is necessary to use correct measurements from actual system inputs and outputs. This requires the use of techniques for the detection and isolation of sensor faults. In this paper an approach based on analytical redundancy which uses dynamic observers is suggested to solve the sensor fault detection and isolation problem for a single-shaft industrial gas turbine. The proposed technique requires the generation of classical residual functions obtained with different observer configurations. The diagnosis is performed by checking fluctuations of these residuals caused by faults.

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