Fault diagnosis for rotating machinery based on artificial immune algorithm and evidence theory

Along with the continuous development of science and technology, the structures of rotating machinery become to be larger scale and more complicated, which results in higher probability of concurrent fault under actual working conditions. In order to achieve concurrent fault diagnosis for rotating machinery, an integrated method using artificial immune algorithm and evidence theory is proposed in this research work. The self-nonself recognition mechanism of artificial immune system for data analysis and processing has been derived from the negative selection algorithm. Five kinds of dimensionless immune detectors are generated based on negative selection algorithm, then the local diagnosis result of dimensionless immune detector is gotten. Combining with evidence theory fusion rules, the final diagnosis can be obtained. Experimental result demonstrates that the method can realize effectively concurrent fault diagnosis for rotating machinery.