Mathematics and Statistics for Business, Management and Finance

Swift, Louise; Spiezia, Federica

doi:10.1007/978-1-349-14587-4

Cited by 1 publication

(1 citation statement)

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“…First, they are differentiable (a key factor for backpropagation training). Second, they are monotonic and consequently they have no minimum, and hence will not create local error minima which would trap the network during training (Swift, 1997). Given in equation 3 is the simple pure linear function f p , which can prove useful as a transfer function in the output layer of backpropagation networks.…”

Section: Stage Three: Data Collectionmentioning

confidence: 99%

An artificial intelligence approach for improving plant operator maintenance proficiency

Edwards

Holt

Robinson³

2002

Journal of Quality in Maintenance Engineering

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Construction plant maintenance practice and its plant operators are inextricably linked. This is because, unlike plant operating within the manufacturing sector, construction plant is largely dependent upon operator skill and competence to maintain the item in a safe, fully operational condition. Research has previously successfully modelled machine breakdown, but revealed that the operator’s impact upon machine breakdown rates can be considerable. A conceptual model methodology with which to assess the maintenance proficiency of individual plant operators is presented. Specifically, an artificial intelligent classification model is proposed as a means of classifying plant operator maintenance proficiency into one of three bandings. These are good, average and poor. The results of such work will form the basis of new prescriptive guidelines, for incorporation into the new certificate of training achievement (CTA) scheme, available to inexperienced construction plant operators. The paper concludes with an indication of the palpable benefits of such research, to plant owners and the construction industry at large.

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Section: Stage Three: Data Collectionmentioning

confidence: 99%