No abstract

Unbalance is a common machinery fault, which occurs because of uneven distribution of mass of a rotating component about its axis. Vibration-based monitoring techniques have been widely accepted for machinery fault diagnosis. This article presents experimental verification of a recently developed Kalman filter–based method for the identification of unbalance in rotor systems. The method is tested on an experimental test rig for different unbalance configurations and shaft speeds. The proposed technique is a model-based method, which requires a mathematical model of the rotor system along with response measurements. A rigid rotor model is used, and measured accelerations at bearing pedestals are used for unbalance parameter estimation. Bearing stiffnesses are estimated using a frequency domain parameter estimation technique with measured unbalance responses. Sensitivity analysis is also performed by altering the values of these estimated stiffnesses.

No abstract

Data mining is a process by which the from raw data information and important patterns are estimated. That involves the intermediate processes to find the data patterns from the input datasets. These processes are preprocessing, algorithm implementation and the testing of developed model. algorithm are used find the data pattern from the data, that may in form of associative, any data structure based or weight based. The proposed work is an effort in order to develop an association rule mining algorithm using the decision tree. Where the decision tree is used first to find the decision rules and according to rules new associate rules are extracted from the data.

No abstract