Applications of Hidden Markov Models

Applications of Hidden Markov Models

Predictive maintenance is one of many applications of HMMs and it has become an increasingly popular topic with the increase in the use of IIoT devices as they lets us get knowledge about the conditions of the systems. Besides detecting the current health and performance of the system, predicting the expected failure of a system is also possible with HMMs. Making these predictions accurately is extremely beneficial for industry since it decreases the maintenance costs significantly and lets the production continue without any interruption. The value of these predictions becomes especially important in the cases that auxiliary equipment storage facilities are limited or expensive. In this project I developed a model to predict remaining useful life(RUL) of the Turbofan engines at the Turbofan Engine Degradation Simulation Data Set from NASA Ames Prognostics Data Repository.

Project Poster: 

Project Members: 

Hakan Şirin

Project Advisor: 

Ali Taylan Cemgil

Project Status: 

Project Year: 

  • Spring

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