Evaluation of Methods used for Separation of Vibrations Produced by Gear Transmissions

Abstract

This paper evaluates methods used for separating vibrations produced by a gear transmission from the vibration signal acquired on the gearbox. The paper presents a novel method for evaluating the algorithms used for this separation. The evaluation method takes into account the statistical reliability of the results achieved on multiple sets of signals acquired on the same machine and conditions. The signal separation was applied in order to process data obtained during an experiment carried out with the aim of analyzing the influence of a torque load affecting a gearbox on the vibrations produced by the gear transmission. It is supposed that the vibration characteristics of the gear transmission are strongly affected by the value of the torque load influencing the gearbox shafts. This influence is analyzed using the vibration signal acquired on the gearbox housing. The vibration signal contains significant disturbances, and its interpretation is unclear. The vibration signal generated by the gear transmission can be separated using methods that make it possible to select the valid features included in the signal. Methods for feature selection which implement a systematic search in the state space and methods based on the genetic algorithm were applied. The genetic algorithm poses a robust stochastic global search in the state space that is well suited to deal with nonlinear problems and also shortens the necessary computing time. The evaluation and comparison of the results achieved during the separation process using different methods have to be taken into account. In the case of signal separation, it is important to evaluate differences between the results achieved during particular executions of the separation process performed by the same method on different datasets which were acquired in the case of the same experiment and conditions. Methods with results that vary, or that are different from the results given by other methods, are assumed not to be statistically reliable. It is also necessary to penalize methods leading to results that can vary greatly in some executions according to the scatter data. Conversely, methods that give results varying around the right set of features seem more acceptable. A novel method for rating the statistical reliability of the results has been proposed. This method is essential for methods using a stochastic search in the state space.