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Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the wellknown Mozetic’s approach, a novel hierarchical modelbased diagnosis methodology is put forward for improving efficiency of multifault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used: one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.
Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic’s approach, a novel hierarchical model based on diagnosis principle is put forward for improving efficiency of multifault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used: one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are to show the superiority of the presented technique.