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The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed...
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 of 3 dominant features from the 16-dimensional...
In this paper, four data-driven classification approaches, that is, K-nearest neighbors (K-NN), self-organizing map (SOM), multi-layer perceptron (MLP), and Bayesian Network classifier (BNC), are applied to a health condition monitoring problem for the wearing cutter. The dataset is produced from a cutting machine using force sensing. A genetic algorithm (GA) based search is performed to select 3...
Partial discharge (PD) detection has been used in assessment of condition reliability of electrical insulation in high voltage equipment such as power station. Unfortunately, PD signals took during condition monitoring are often corrupted with excessive interference. The challenge to effectively and accurately determine and extract the pure PD signal from the large amount of noise still remains. The...
The effective approach to monitor the health state of equipment has long been a concern of industrial applications. This paper focuses on the categorical data analysis to build equipment degradation model for predicting equipment failure and monitoring the health state of equipment. Since large volumes of categorical data are generated and collected in most manufacturing execution systems, how to...
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