Ranking and selection of optimum alternatives based on linear scale transformation for cylindrical roller bearing

Abstract

Bearing is the most precautious element in rotating machinery. Defects produced in any part of the bearing may lead to a hazardous environment that increases the noise and vibration. It can be diminished with appropriate loading combinations. The selection of optimum loading conditions requires developing an algorithm. Multiple Attribute Decision Making (MADM) techniques are utilized to pick up a unique and ideal solution or ranking of choices from the available alternatives. The MADM techniques have not been used for bearing fault diagnosis so far. This paper offers a novel methodology for the Ranking and Selection of Optimum Alternative based on Linear Scale Transformation (RSOA-LST), having combined benefit and cost criteria that rank the choices and determine the percentage influence of various alternatives. This methodology has been performed with several experimental signals of cylindrical roller bearing, acquired from twenty-four bearing conditions having several speeds and loading combinations. The normalization of attributes (statistical features) considering combined benefit-cost criteria, mean of various parameters (speeds, attributes, and bearing conditions), and Percentage Influencing (PI) are intended simultaneously to obtain the ranking of alternatives (loading conditions). Local Defect Analysis (LDA) and Global Defect Analysis (GDA) are carried out in this research. In LDA, the ranking of alternatives based on the PI for each defect and the ranking of bearings based on fluctuation in PI of various alternatives are attained. In GDA, the sequence of alternatives is obtained globally. The effectiveness of the suggested methodology has been verified over other MADM techniques. The outcomes state that the suggested technique effectively bifurcates the influence of alternatives locally as well as globally over other techniques.