Omid Geramifard

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In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state(More)
In this paper, two temporal models, Hidden Markov Model and Auto Regressive Moving Average model with exogenous inputs (ARMAX), are used for health condition monitoring of the cutter in a milling machine. Dataset is acquired through real time force signal sensing. A heuristic statistical approach is used to select dominant features, leading to the selection(More)
One of the challenging tasks in the domain of Tool Condition Monitoring (TCM) is feature selection. Feature selection is crucial as extracting all possible features and creating a model based on those features results in two major disadvantages, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. In this paper,(More)
In this paper, a power-signature-based Bayesian multi-classifier is proposed to identify various operational modes of a complex machinery system that can help determine the energy contribution of different operation modes, identify potential energy hot-spots and provide basis for more accurate energy consumption calculation. This technology can also help(More)
In this paper, we study the use of historical data from a fleet (i.e., a group) of equipment to improve condition monitoring and health assessment for each individual equipment (within the fleet) for maintenance purposes. In particular, we propose a fleet-based approach to estimate the tool wear of milling machines at arbitrary operating conditions (OCs)(More)
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