In this paper, a neural network-based method of wire rope fault prediction in a system is proposed. This method is developed based on past observations on various wire rope parameters for lock coil rope, for example number of rope used in the system, period of test, number of faults,
etc. To capture the data from various systems, with a view to improving the prediction accuracy, we have designed a multi-layer perceptron network (MLP) to realise better performance.
Magnetic nondestructive evaluation is being used to assess the condition of full locked coil ropes used in cage and skip winders. Four locked coil ropes were studied for a period of 4 years. This paper focuses on the determination of the in situ condition of full locked coil ropes in cage and skip winders throughout this 4-year period.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.