New algorithms for diagnosing defects of an air-operated valve for self diagnostic monitoring system

We have developed a self-diagnostic monitoring system for an air operated valve system which produces arrow patterns according to the states of the system and makes a diagnosis whenever the system shows the corresponding symptom [1, 2]. In our first model, we have used a neural network and a simple comparison method for decision processor. In this paper, we modify and improve the decision processor module. We developed a logistic regression algorithm for the simple decision algorithm and modified the neural network algorithm. By changing the rule for translating arrow symbols into 2-D tuples, we could make unambiguous and rich training data set. With this, we performed some simulations and present a result.