There is a growing interest in using permanently installed sensors to monitor for defects in engineering components; the ability to collect real-time measurements is valuable when evaluating the structural integrity of the monitored component. However, a challenge in evaluating the detection capabilities of a permanently installed sensor arises from its fixed location and finite field-of-view, combined with the uncertainty in damage location. A probabilistic framework for evaluating the detection capabilities of a permanently installed sensor is thus proposed. By combining the spatial maps of sensor sensitivity obtained from model-assisted methods and probability of defect location obtained from structural mechanics, the expectation and confidence in the probability of detection (POD) can be estimated. The framework is demonstrated with four sensor-component combinations, and the results show the ability of the framework to characterise the detection capability of permanently installed sensors and quantify its performance with metrics such as the $${\mathrm{a}}_{90|95}$$ a 90 | 95 value (the defect size where there is 95% confidence of obtaining at least 90% POD), which is valuable for structural integrity assessments as a metric for the largest defect that may be present and undetected. The framework is thus valuable for optimising and qualifying monitoring system designs in real-life engineering applications.

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The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.

Positive-feedback mechanisms such as fatigue induce a self-accelerating behavior, captured by models displaying infinite limit-state asymptotics, collectively known as the failure forecast method (FFM). This paper presents a Bayesian model parameter estimation approach to the fully nonlinear FFM implementation and compares the results to the classic linear regression formulation, including a regression uncertainty model. This process is demonstrated in a cyclic loading fatigue crack propagation application, both on a synthetic data set and on a full fatigue experiment. A novel "switch point" parameter is included in the Bayesian formulation to account for nonstationary changes in the growth parameter.

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