A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
The actual condition of a system may not be identical to its apparent condition as reported from inspections. The apparent condition can be biased by non-detection of actual flaws, false calls of non-existent defects, incorrectly characterized indications, and lack of results from uninspected regions. Reliability analysis tools, such as probability of detection, false call estimation, and assessment of sizing uncertainty, are commonly used to compensate for the difference between the actual and apparent condition. However, these static corrections neglect the time evolution of the system condition, which may, over the system life cycle, diverge from the condition assumed by its operators. In this work, we model the unobserved, underlying condition of a large system over decades of service life. Maintenance and repair activities, all contingent upon earlier inspection results, are included in the model. The apparent condition at each inspection date is then calculated by filtering the underlying condition through inspection reliability analysis. In this particular case study, a large historical data set permits us to compare predicted inspection results to those obtained over many years. The assumed distribution of degradation rates, effectiveness of repair, and inspection reliability parameters can then be adjusted to provide a more accurate picture of the actual system condition. The history and predicted future life of a system are best obtained from this type of model, which permits evaluation of alternative scenarios for inspection priorities, assessment of repair effectiveness, and meaningful planning for end-of-life or life extension.
The actual condition of a system may not be identical to its apparent condition as reported from inspections. The apparent condition can be biased by non-detection of actual flaws, false calls of non-existent defects, incorrectly characterized indications, and lack of results from uninspected regions. Reliability analysis tools, such as probability of detection, false call estimation, and assessment of sizing uncertainty, are commonly used to compensate for the difference between the actual and apparent condition. However, these static corrections neglect the time evolution of the system condition, which may, over the system life cycle, diverge from the condition assumed by its operators. In this work, we model the unobserved, underlying condition of a large system over decades of service life. Maintenance and repair activities, all contingent upon earlier inspection results, are included in the model. The apparent condition at each inspection date is then calculated by filtering the underlying condition through inspection reliability analysis. In this particular case study, a large historical data set permits us to compare predicted inspection results to those obtained over many years. The assumed distribution of degradation rates, effectiveness of repair, and inspection reliability parameters can then be adjusted to provide a more accurate picture of the actual system condition. The history and predicted future life of a system are best obtained from this type of model, which permits evaluation of alternative scenarios for inspection priorities, assessment of repair effectiveness, and meaningful planning for end-of-life or life extension.
The time evolution of actual condition and apparent condition for an inspected system
Horn, Dag (author)
2013
8 Seiten, 4 Bilder, 3 Quellen
Conference paper
Storage medium
English
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