Local Termination Criterion for Impulsive Component Detection Using Progressive Genetic Algorithm

A problem of local damage detection for condition monitoring based on vibration data can be approached from many different angles. One of the most common ways is selective filtration of the vibration signal. There are many techniques allowing to construct digital filter for particular input data (e.g. spectral selectors). In previous articles authors proposed a technique called Progressive Genetic Algorithm (PGA) to optimally design digital filter for a given data set using no prior assumptions. It uses kurtosis as fitness function and local linear fit of fitness function progression vector as a global termination criterion (GTC), but local termination criterion (LTC) was defined as simple stall limit of fitness value. In this paper authors propose a new quantile-based way to terminate PGA locally for faster convergence. Initial testing phase shows that for comparable quality of obtained result, individual epochs terminate significantly faster without sacrificing the progress of local convergence. It results in more efficient optimization and faster global convergence which reduces the overall execution time of the program for about the order of magnitude.