FAULTS DETECTION IN GAS TURBINE ROTOR USING VIBRATION ANALYSIS UNDER VARYING CONDITIONS

Monitoring of rotating machines is a very important task in most industrial sectors, which requires a chosen number of performance indicators during the exploitation of such kind of equipments. Indeed, for understanding the undesirable phenomena complexity of the industrial systems under operation, a reliable and an accurate mathematical modeling is required to ensure the diagnosis and the control of these phenomena. This work proposes development of a fault monitoring system of a gas turbine type GE MS 3002 based on vibration analysis technique using spectral analysis tools. The obtained results prove the effectiveness of the presented monitoring tool approach which is applied on the gas turbine,for avoiding the operation under vibration mode and for generating optimal performance during the exploitation of the gas turbine

ANFIS Models for Fault Detection and Isolation in the Drive Train of a Wind Turbine

The paper aims to improve the fault detection and isolation process in wind turbine systems by developing intelligent systems that can effectively identify and isolate faults. Specifically, the paper focuses on the drive train part of a horizontal axis wind turbine machine. The proposed fault diagnostic strategy is designed using an adaptive neural fuzzy inference system (ANFIS), which is a type of artificial neural network that combines the advantages of both fuzzy logic and neural networks. The ANFIS is used to generate residuals that occur after faults have been detected, and to determine the appropriate thresholds needed to correctly detect faults. The simulation results show that the proposed fault diagnostic strategy is effective in detecting faults in the drive train part of the wind turbine system. By using intelligent systems such as ANFIS, the fault detection process can be automated and streamlined, potentially reducing maintenance costs and improving the overall performance and efficiency of wind turbine systems

Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

In industrial practice, the representation of the dynamics of nonlinear systems by models linking their different operating variables requires an identification procedure to characterize their behavior from experimental data. This article proposes the identification of the variables of a two-shafts gas turbine based on a decoupled multi-model approach with genetic algorithm. Hence the multi-model is determined in the form of a weighted combination of the decoupled linear local state space sub-models, with optimization of an objective cost function in different modes of operation of this machine. This makes it possible to have robust and reliable models using input / output data collected on the examined system, limiting the influence of errors and identification noises

Evaluation of Reliability Indices for Gas Turbines Based on the Johnson SB Distribution: Towards Practical Development

Recent advancements in computer engineering have provided effective solutions for processing and analyzing complex systems and big data. Consequently, the adjustment and standardization of this data play a crucial role in addressing issues related to the monitoring of industrial systems. In this study, we propose a reliability approach for gas turbines to identify and characterize their degradation using operational data. We introduce a method for adjusting turbine reliability data, which resolves the challenges associated with the nature of these operating data. This enables us to determine a mathematical function that models the relationships between turbine reliability parameters and evaluate the impact of reliability practices in terms of availability. Additionally, we determine the survival function and employ it as a lifespan distribution model by estimating the parameters of the Johnson SB function. Furthermore, we calculate the failure rates and mean time between good operations for this rotating machine under different operating conditions. The obtained results allow us to estimate the parameters of the distribution that best fit the turbine reliability data, which are validated through statistical and graphical tests. We assess the goodness-of-fit using mean square error and reliability tests such as Kolmogorov-Smirnov

PARAMETRIC IDENTIFICATION AND STABILIZATION OF TURBO-COMPRESSOR PLANT BASED ON MATRIX FRACTION DESCRIPTION USING EXPERIMENTAL DATA

This paper deals with the application of the multivariable linear system Matrix Fraction Description (MFD) theory for the identification of the Left Linear MIMO ARX turbo-compressor model based on Extended Least Square (ELS) estimator. Indeed, the identification of studied system model parameters is achieved using the experimental (inputs/outputs) data, which have been obtained by measurement on site where the main aim is to ensure the selection of the best model with minimum order. For the validation of the obtained results, a comparative study has been performed with the Hammerstein-Wiener model based on validation criteria where the reliability of the obtained model is taken into account. Finally, a right/left block solvent in several canonical forms have been assigned to present a comparative study, where the objective to ensure the stability enhancement of the dynamic behaviour of the studied turbo-compressor

A robust fault diagnosis and forecasting approach based on Kalman filter and interval type-2 fuzzy logic for efficiency improvement of centrifugal gas compressor system

The paper proposes a robust faults detection and forecasting approach for a centrifugal gas compressor system, the mechanism of this approach used the Kalman filter to estimate and filtering the unmeasured states of the studied system based on signals data of the inputs and the outputs that have been collected experimentally on site. The intelligent faults detection expert system is designed based on the interval type-2 fuzzy logic. The present work is achieved by an important task which is the prediction of the remaining time of the system under study to reach the danger and/or the failure stage based on the Auto-regressive Integrated Moving Average (ARIMA) model, where the objective within the industrial application is to set the maintenance schedules in precisely time. The obtained results prove the performance of the proposed faults diagnosis and detection approach which can be used in several heavy industrial systemsPeer ReviewedPostprint (published version

Diagnostic et surveillance des processus industriels complexes par FDI floue (fuzzy fault detection and isolation): application à un système de compression

158 p. : ill. ; 30 c