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The fault diagnosis inverse problem with Ant Colony Optimization and Ant Colony Optimization with dispersion

机译：蚁群优化和离散度蚁群优化的故障诊断逆问题

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摘要

This paper is focused on the formulation of fault diagnosis (FDI) using an inverse problem methodology. The FDI inverse problem is formulated as an optimization problem which is solved by bio-inspired algorithms. In this case, the algorithms Ant Colony Optimization (ACO), and its modified version ACO-d have been applied. This approach combines results from FDI area for making an alternative uniqueness analysis of the FDI inverse problem, which is related with detectability and isolability of faults, components of the diagnosis. The proposed approach is tested using simulated data from the Inverted-Pendulum System which is recognized as a benchmark for control and diagnosis. This work also studies the influence of ACO and ACO-d parameters in order to obtain a robust (to external disturbances) and sensitive (to incipient faults) diagnosis. The results show the suitability of the approach. They also indicate that parameters values allowing a greater diversification of the search, yield a better diagnosis. The ACO-d algorithm enables better diagnosis than ACO.
机译：本文着重于使用逆问题方法论来制定故障诊断（FDI）。 FDI逆问题被公式化为一个优化问题，可以通过生物启发算法来解决。在这种情况下，已经应用了蚁群优化算法（ACO）及其修改版本ACO-d。这种方法结合了FDI领域的结果，可以对FDI逆问题进行替代性唯一性分析，这与故障的可检测性和可隔离性（诊断的组成部分）有关。使用倒立摆系统的模拟数据对提出的方法进行了测试，该系统被认为是控制和诊断的基准。这项工作还研究了ACO和ACO-d参数的影响，以获得鲁棒的（对外部干扰）和敏感的（对初期故障）诊断。结果表明该方法的适用性。它们还表明允许更大范围地进行搜索的参数值可产生更好的诊断。 ACO-d算法比ACO具有更好的诊断能力。

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