Detecting the fault from spectrograms by using genetic algorithm techniques

During the last thirty years there has been a rapidly growing interest in the field of genetic algorithms (GAs). The field is at a stage of tremendous growth, as evidenced by the increasing number of conferences, workshops, and papers concerning it, as well as the emergence of a central journal for the field. With their great robustness, genetic algorithms have proven to be a promising technique for many optimisation, design, control, and machine learning applications. This paper presents a new technique for detecting the source of fault in spinning mills from spectrograms by using genetic algorithm.