Incipient Fault Detection and Diagnosis in Turbine Engines Using Hidden Markov Models

2003 ◽  
Author(s):  
Sunil Menon ◽  
O¨nder Uluyol ◽  
Kyusung Kim ◽  
Emmanuel O. Nwadiogbu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Markov Models ◽  
Hidden Markov ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Turbine Engines ◽  
Incipient Fault ◽  
Detection And Diagnosis ◽  
Incipient Fault Detection

Incipient fault detection and diagnosis in turbine engines is key to effective maintenance and improved availability of systems dependent on these engines. In this paper, we present a novel method for incipient fault detection and diagnosis using Hidden Markov Models (HMMs). In particular, we focus on engine faults that are manifest in transient operating conditions such as engine startup and acceleration. HMMs are stochastic signal models that are effective in modeling transient signals. They are developed with engine data collected under nominal operating conditions. Engine data representing different fault conditions are used to develop the fault HMMs; a separate model is developed for each of the faults. Once the nominal and fault HMMs are developed, new engine data collected from the engine are evaluated against the HMMs and a determination is made whether a fault is indicated. Here, we demonstrate our HMM-based fault detection and diagnosis approach on engine speed profiles taken from a real engine. Further, the effectiveness of the HMM-based approach is compared with a neural-network-based approach and a method based on using principal component analysis in conjunction with a neural network approach.

Turbine Engine Modeling Using Temporal Neural Networks for Incipient Fault Detection and Diagnosis

2004 ◽  
Author(s):  
Sunil Menon ◽  
O¨nder Uluyol ◽  
Deepanker Gupta
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Turbine Engine ◽  
Echo State Networks ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Detection And Diagnosis ◽  
Incipient Fault Detection

We present a method of fault detection and diagnosis in turbine engines using temporal neural networks. Temporal neural networks allow us to represent the complete engine operating range by complementing the first-principle models which are usually restricted to takeoff and cruise phases. Because faults that are manifest only in particular phases can be detected, complete coverage leads to more accurate anomaly detection and fault diagnosis systems. The time series sensor data from the engine is collected during particular aircraft flight phases such as startup, takeoff, cruise, and shutdown. We use the echo state network to develop an incipient fault detection and diagnosis system. Echo state networks have several advantages over conventional types of temporal neural networks, including accuracy and ease of training. We demonstrate the efficacy of using the echo state networks to focus on flight phases that are difficult to model. We present results of our fault detection and diagnosis method with actual propulsion engine transient flight data.

scholarly journals Real Time Monitoring and Neuro-Fuzzy Based Fault Diagnosis of Flow Process in Hybrid System

2012 ◽  
pp. 171-177
Author(s):  
Revathi. P ◽  
Pallikonda Rajasekaran. M ◽  
Babiyola. D ◽  
Aruna. R
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Real Time ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Economic Losses ◽  
Monitoring Systems ◽  
Back Propagation Algorithm ◽  
System Failures ◽  
Detection And Diagnosis

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as open global standard to address the low-cost, low-power wireless sensor networks. The goal is to monitor the parameters and to classify the parameters in normal and abnormal conditions to detect fault in the process as early as possible by using artificial intelligent techniques. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. Several techniques are being investigated as an extension to the traditional fault detection and diagnosis. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes ANFIS (Adaptive Neural Fuzzy Inference System) for fault detection and diagnosis. In ANFIS, the fuzzy logic will create the rules and membership functions whereas the neural network trains the membership function to get the best output. The output of ANFIS is compared with Back Propagation Algorithm (BPN) algorithm of neural network. The training and testing data required to develop the ANFIS model were generated at different operating conditions by running the process and by creating various faults in real time in a laboratory experimental model.

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