Wavelet-Based SNR Analysis in Building Satellite Terminal Fault Identification System

2008 ◽  
Author(s):  
L. Xu ◽  
C. Huang
Keyword(s):  
Fault Identification ◽  
Identification System

New Model Based Gas Turbine Fault Diagnostics Using 1D Engine Model and Nonlinear Identification Algorithms

2009 ◽  
Author(s):  
Seyyed Hamid Reza Hosseini ◽  
Hiwa Khaledi ◽  
Mohsen Reza Soltani
Keyword(s):  
Gas Turbine ◽  
Diagnostic System ◽  
Gas Turbine Engine ◽  
Fault Model ◽  
Fault Identification ◽  
Identification System ◽  
Turbine Engine ◽  
Engine Model ◽  
Exhaust Temperature ◽  
Model Based

Gas turbine fault identification has been used worldwide in many aero and land engines. Model based techniques have improved isolation of faults in components and stages’ fault trend monitoring. In this paper a powerful nonlinear fault identification system is developed in order to predict the location and trend of faults in two major components: compressor and turbine. For this purpose Siemens V94.2 gas turbine engine is modeled one dimensionally. The compressor is simulated using stage stacking technique, while a stage by stage blade cooling model has been used in simulation of the turbine. New fault model has been used for turbine, in which a degradation distribution has been considered for turbine stages’ performance. In order to validate the identification system with a real case, a combined fault model (a combination of existing faults models) for compressor is used. Also the first stage of the turbine is degraded alone while keeping the other stages healthy. The target was to identify the faulty stages not faulty components. The imposed faults are one of the most common faults in a gas turbine engine and the problem is one of the most difficult cases. Results show that the fault diagnostic system could isolate faults between compressor and turbine. It also predicts the location of faulty stages of each component. The most interesting result is that the fault is predicted only in the first stage (faulty stage) of the turbine while other stages are identified as healthy. Also combined fault of compressor is well identified. However, the magnitude of degradation could not be well predicted but, using more detailed models as well as better data from gas turbine exhaust temperature, will enhance diagnostic results.

scholarly journals Development of Fault Identification System for Electric Servo Actuators of Multilink Manipulators Using Logic-Dynamic Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
V. Filaretov ◽  
A. Zuev ◽  
A. Zhirabok ◽  
A. Protsenko
Keyword(s):  
Fault Detection ◽  
Nonlinear Equations ◽  
Fault Detection And Isolation ◽  
Fault Identification ◽  
Identification System ◽  
Dynamic Approach ◽  
Residual Signal

This paper presents the method of synthesis of faults identification systems for electric servo actuators of multilink manipulators. These actuators are described by nonlinear equations with significantly changing coefficients. The proposed method is based on logic-dynamic approach for design of diagnostic observers for fault detection and isolation. An advantage of this approach is that it allows studying systems with nonsmooth nonlinearities by linear methods only. For solving the task of faults identification, a residual signal feedback was proposed to be used for observers. The efficiency of the proposed fault identification system was confirmed by results of simulation.

Model Based Compressor Fault Identification Using Stage Stacking Technique and Nonlinear Diagnostic Systems

2008 ◽  
Author(s):  
Seyyed Hamid Reza Hosseini ◽  
Hiwa Khaledi ◽  
Mohammad Bagher Ghofrani
Keyword(s):  
Pressure Rise ◽  
Diagnostic System ◽  
Flow Coefficient ◽  
Single Type ◽  
Fault Identification ◽  
Identification System ◽  
Diagnostic Systems ◽  
Model Based ◽  
Systems Model ◽  
Two Stages

Compressor fault identification is an important part of gas turbine diagnostic systems. Model based techniques have been used widely in this field. In this paper the performance of two compressors has been simulated using stage stacking method. One of them is the NASA 10 stage constant tip diameter compressor. The other one is the Siemens V94.2 engine’s compressor. Then a new and more real compressor fault model is introduced and the effect of different faults on compressor performance has been studied. In this paper an intelligent fault diagnostic system has been developed which is able to detect different faults of the compressor. In all of the cases of faults, degradation in inlet flow coefficient of the compressor has been considered besides degradation of pressure rise coefficient and stages’ efficiencies, which has not been considered in any available study. It is shown that the fault identification system is very powerful in detecting single type faults. In the case of combined faults it was found that the faults which have been distributed in more than two stages will be identified.

scholarly journals Feature extraction and recognition of rotating machinery fault noise based on convolutional neural networks

2021 ◽  
Vol 2113 (1) ◽  
pp. 012014
Author(s):  
Yongshao Xu ◽  
Bingzheng Liu ◽  
Haotian Shang ◽  
Yueqi Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Detection Method ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Identification System ◽  
Acoustic Detection ◽  
Rotating Machine ◽  
Long Time ◽  
Non Destructive

Abstract Rotating machines are common equipment in industrial production, which may cause failure for a long time. Because of its convenient use and non-destructive to itself, acoustic detection method is suitable for fault diagnosis of rotating machinery. The convolution neural network model is used to identify several typical rotating machine faults. The repeatability experiments and different training sets show that the method has good universality. A visual fault identification system is built, and the effect of the system is verified by experiments.

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