Fault Diagnosis with Missing Data Based on Hopfield Neural Networks

Raquelita Torres Cabeza (), Egly Barrero Viciedo (), Alberto Prieto-Moreno () and Valery Moreno Vega ()
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Raquelita Torres Cabeza: Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Automatic and Computing Department
Egly Barrero Viciedo: Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Automatic and Computing Department
Alberto Prieto-Moreno: Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Automatic and Computing Department
Valery Moreno Vega: Instituto Superior Politécnico José Antonio Echeverría (CUJAE), Automatic and Computing Department

Chapter Chapter 3 in Mathematical Modeling and Computational Intelligence in Engineering Applications, 2016, pp 37-46 from Springer

Abstract: Abstract Most of the existing artificial neural network models use a significant amount of information for their training. The need for such information could be an inconvenience for its application in fault diagnosis in industrial systems, where the information, due to different factors such as data losses in the data acquisition systems, is scarce or not verified. In this chapter, a diagnostic system based on a Hopfield neural network is proposed to overcome this inconvenience. The proposal is tested using the development and application of methods for the actuator diagnostic in industrial control systems (DAMADICS) benchmark, with successful performance.

Keywords: Fault diagnosis; Hopfield neural networks; Industrial processes; Quality of data; Incomplete data; Missing data; DAMADICS (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-38869-4_3

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DOI: 10.1007/978-3-319-38869-4_3

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