Condition Surveillance for Plant Rotating Machinery Using a Fuzzy Neural Network

Abstract—Condition surveillance of rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. In a large plant, because there are an enormous number of rotating machines, condition surveillance for all rotating machines is time consuming and labor intensive and the accuracy of condition judgment is not ensured. These difficulties may cause serious machine accidents and great production losses. In order to improve the efficiency of the condition surveillance and detect faults at an early stage, the present paper proposes a method of condition surveillance for plant rotating machinery using a “Partially-Linearized Neural Network (PLNN)” by which the state of a rotating machine can be automatically judged on the basis of the possibility of normal and abnormal states. When input the rotating speed, power, shaft diameter and vibration parameter (root mean square, RMS) of a rotating machine into the PLNN, after learning has been completed, the PLNN will output the possibilities of normal and abnormal states for the inspected rotating machine. Practical examples of condition surveillance for plant rotating machinery will verify that the proposed method is effective.