Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network

Damage mechanisms that affect components within complex machines are often hard to detect and identify, especially if they are difficult to access, inspect and/or that are under continuous duty, compromising the reliability and performance of systems. In this paper, a Bayesian network model is developed to handle the interactions among common damage mechanisms and failure modes in nuclear steam turbine rotating blades. This model enables maintenance and inspection planning to better predict which portions(s) of the turbine will need repair. To compute the conditional probability tables, the model's unique quantification method combines expert judgement, the Recursive Noisy OR, and a damage mechanism susceptibility ranking that takes into account the synergistic interactions of the damage mechanisms. The approach can be suited to different turbine designs and purposes. The Bayesian network model development is described in detail, validated, and several examples of its application are presented.

Automated summary

The paper introduces a Bayesian network model to handle interactions among common damage mechanisms and failure modes in nuclear steam turbine rotating blades. This model helps predict which portions of the turbine will need repair and utilizes a unique quantification method combining expert judgement, Recursive Noisy OR, and damage mechanism susceptibility ranking. It is adaptable to different turbine designs and purposes, with detailed development, validation, and examples of application presented.