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A swarm intelligence-based approach to multiple-fault diagnostics for industrial applications is proposed. Drawbacks of swarm-based algorithms in heuristic search strategy related to mutual dependence of solutions are overcome by a likelihood-based trail intensity modification of ant-colony optimization. Numerical results of a comprehensive characterization through statistical experiment design on high-dimension multiple-faults diagnosis applications are shown. Experimental results under the framework of an industrial research project committed to industrial remote monitoring of operating machines are discussed. Numerical and experimental results show excellent performance, outperforming genetic algorithms, especially in high-dimension problems, and easiness in algorithm configuration.