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

1999 ◽  
Author(s):  
R. Bettocchi ◽  
P. R. Spina
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Digital Filters ◽  
Fault Isolation ◽  
Measurement Noise ◽  
Canonical Structure ◽  
Sensor Faults ◽  
Single Output ◽  
Auto Regressive

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.

scholarly journals Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Soumalya Sarkar ◽  
Kushal Mukherjee ◽  
Soumik Sarkar ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Symbolic Dynamic ◽  
Turbine Engines ◽  
Test Case ◽  
Gas Turbine Engines ◽  
Transient Time ◽  
Steady State Time ◽  
Dynamic Filtering

This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.

A Unified Nonlinear Approach to Fault Diagnosis of Aircraft Engines

2013 ◽  
Author(s):  
Xiaodong Zhang ◽  
Remus C. Avram ◽  
Liang Tang ◽  
Michael J. Roemer
Keyword(s):  
Fault Detection ◽  
Adaptive Estimation ◽  
Fault Isolation ◽  
Nonlinear Observer ◽  
Aircraft Engines ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Engine Model ◽  
Fault Type ◽  
Component Faults

Many existing aircraft engine diagnostic methods are based on linearized engine models. However, the dynamics of aircraft engines are highly nonlinear and rapidly changing. Future engine health management designs will benefit from new methods that are directly based on intrinsic nonlinearities of the engine dynamics. In this paper, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation and nonlinear observer techniques. Engine sensor faults, actuator faults and component faults are considered under one unified nonlinear framework. The fault diagnosis architecture consists of a fault detection estimator and a bank of nonlinear fault isolation estimators. The fault detection estimator is used for detecting the occurrence of a fault, while the bank of fault isolation estimators is employed to determine the particular fault type or location after fault detection. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. Specifically, adaptive estimation techniques are used for designing the isolation estimators for engine component faults and actuator faults, while nonlinear observer techniques are used for designing the isolation estimators for sensor faults. The FDI architecture has been integrated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine model developed by NASA researchers in recent years. The engine model is a realistic representation of the nonlinear aero thermal dynamics of a 90,000-pound thrust class turbofan engine with high-bypass ratio and a two-spool configuration. Representative simulation results and comparative studies are shown to verify the effectiveness of the nonlinear FDI method.

Fuzzy robust fault estimation scheme for fault tolerant flight control systems based on unknown input observer

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gulay Unal
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fault Detection ◽  
Flight Control ◽  
Fault Tolerant ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Flight Control System ◽  
Content Type

Purpose The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely important in obtaining healthy flight. An approach to detect and isolate aircraft sensor faults is proposed, and a new integrated structure for a fault tolerant aircraft control system is presented. Design/methodology/approach As disturbance and sensor faults are mixed together in a flight control system, it is difficult to isolate any fault from the disturbance. This paper proposes a robust unknown input observer for state estimation and fault detection as well as isolation using fuzzy logic. Findings The dedicated observer scheme (DOS) and generalized observer scheme (GOS) are used for fault detection and isolation in an observer-based approach. Using the DOS, it has been shown through simulation that sensor fault detection and isolation can be made, but here the threshold value must be well chosen; if not, the faulty sensor cannot be correctly isolated. On the other hand, the GOS is more usable and flexible than the DOS and allows isolation of faults more correctly and for a fuzzy logic-based controller to be used to realize fault isolation completely. Originality/value The fuzzy logic approach applied to the flight control system adds an important key for sensor fault isolation because it reduces the effect of false alarms and allows the identification of different kinds of sensor faults. The proposed approach can be used for similar systems.

Comparison of a Few Fault Diagnosis Methods on Sparse Variable Length Time Series Sequences

2007 ◽  
Author(s):  
Lokesh Kumar Sambasivan ◽  
Venkataramana Bantwal Kini ◽  
Srikanth Ryali ◽  
Joydeb Mukherjee ◽  
Dinkar Mylaraswamy
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Diagnosis ◽  
Variable Length ◽  
Training Data ◽  
Series Data ◽  
Support Vector ◽  
Detection And Diagnosis ◽  
Time Sequences

Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.

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.

A Two-Layer Multi-Model Gas Path Fault Diagnosis Method

2018 ◽  
Author(s):  
Yunpeng Cao ◽  
Yinghui He ◽  
Fang Yu ◽  
Jianwei Du ◽  
Shuying Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Fault Isolation ◽  
Fuzzy Membership Function ◽  
Distance Method ◽  
Isolation Layer ◽  
Diagnosis Method ◽  
Gas Path Fault

This paper presents a two-layer multi-model gas path fault diagnosis method for gas turbines that includes a fault detection layer and a fault isolation layer. A health model and a gas path fault model based on a back propagation neural network are used for the real-time estimation of the output parameters of a gas turbine in the fault detection layer and the output parameter residual in the fault isolation layer, respectively. A fault detection algorithm is proposed based on fuzzy inference, and the fuzzy membership function of the output parameters residual is realized using data statistics. A similarity distance method is used to realize fault isolation, and a fault probability algorithm based on the Mahalanobis distance is presented. Finally, the proposed method is verified by a three-shaft gas turbine simulation platform, and the simulation test results show that the two-layer multi-model gas path fault diagnosis method can detect and isolate the gas path fault accurately with a low calculation cost and good extensibility.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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