Computing derivatives in interval type-2 fuzzy logic systems trained by steepest descent method for fault classification in a switch machine

A switch machine is an electromechanical device that allows railway trains to be guided from one track to another. Among all possible faults that can occur in a switch machine, the three mains ones are: lack of lubrication, lack of adjustment and malfunction of a component. Aiming to classify these faults, an important contribution of this work is to address the height type-reduction and interval singleton type-2 fuzzy logic system derivatives. The computational simulations are performed with real data set provided by a Brazilian company of the railway sector. The obtained results are compared with other models reported in the literature (Bayes theory, multilayer perceptron neural network and type-1 fuzzy logic system), demonstrating the effectiveness of the proposed classifier and revealing that the proposal is able to properly handle with uncertainties associated with the measurements and with the data that are used to tune the parameters of the model. In addition, the convergence speed and performance analysis show that the proposed interval singleton type-2 fuzzy logic system is attractive for classifying faults in a switch machine.