scholarly journals Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

2008 ◽  
Vol 82 (6) ◽  
pp. 1252-1262 ◽  
Author(s):  
Figen Kosebalaban Tokatli ◽  
Ali Cinar
Keyword(s):  
Fault Detection ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

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.

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