Model development based on evolutionary framework for condition monitoring of a lathe machine
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gp-bibliography.bib Revision:1.8098
- @Article{Garg:2015:Measurementa,
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author = "Akhil1 Garg and V. Vijayaraghavan and K. Tai and
Pravin M. Singru and Vishal Jain and
Nikilesh Krishnakumar",
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title = "Model development based on evolutionary framework for
condition monitoring of a lathe machine",
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journal = "Measurement",
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volume = "73",
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pages = "95--110",
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year = "2015",
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ISSN = "0263-2241",
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DOI = "doi:10.1016/j.measurement.2015.04.025",
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URL = "http://www.sciencedirect.com/science/article/pii/S0263224115002389",
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abstract = "The present work deals with the vibro-acoustic
condition monitoring of the metal lathe machine by the
development of predictive models for the detection of
probable faults. Firstly, the experiments were
conducted to obtain vibration and acoustic signatures
for the three operations (idle running, turning and
facing) used for three experimental studies (overall
acoustic, overall vibration and headstock vibration).
In the perspective of formulating the predictive
models, multi-gene genetic programming (MGGP) approach
can be applied. However, it is effective functioning
exhibit high dependence on the complexity term
incorporated in its fitness function. Therefore, an
evolutionary framework of MGGP based on its new
complexity measure is proposed in formulation of the
predictive models. In this proposed framework,
polynomials known for their fixed complexity (order of
polynomial) are used for defining the complexity of the
generated models during the evolutionary stages of
MGGP. The new complexity term is then incorporated in
fitness function of MGGP to penalize the fitness of
models. The results reveal that the proposed models
outperformed the standardized MGGP models. Further, the
parametric and sensitivity analysis is conducted to
study the relationships between the key process
parameters and to reveal dominant input process
parameters.",
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keywords = "genetic algorithms, genetic programming, Vibration,
Acoustics, Condition monitoring, Machine learning,
Predictive maintenance, Machining modelling",
- }
Genetic Programming entries for
Akhil Garg
Venkatesh Vijayaraghavan
Kang Tai
Pravin M Singru
Vishal Jain
Nikilesh Krishnakumar
Citations