Fatigue life prediction based on Bayesian approach to incorporate field data into probability model

Abstract

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.

Cost-Effectiveness of Structural Health Monitoring in Fuselage Maintenance of the Civil Aviation Industry †

“…When a physics model that can describe the degradation of damage is available, a physics-based algorithm can be utilized [10]. An et al [11] used Bayesian inference to identify unknown model parameters of the crack growth model with damage growth data from SHM sensors, from which the future growth of cracks is predicted. Once the model parameters are identified, it is possible to predict when the damage becomes dangerous, and an appropriate maintenance time can be predicted.…”

Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained results

“…It is used within linear damage accumulation models. Contrary, An et al [32] use Bayesian approach to predict lifetime. The authors of [32] state more accurate prediction results using Bayesian approach in comparison with commonly used damage accumulation models but identify also aggravating factors and limitations of Bayesian approach.…”

“…Contrary, An et al [32] use Bayesian approach to predict lifetime. The authors of [32] state more accurate prediction results using Bayesian approach in comparison with commonly used damage accumulation models but identify also aggravating factors and limitations of Bayesian approach. This results from non-optimal choice of measured data, presence of noise, and uncertainties due to material properties and production processes or similar.…”

Statistical wind prediction and fatigue analysis for horizontal-axis wind turbine composite material blade under dynamic loads

“…They first carried out modal and static analyses to find out blade critical zone for stresses followed by crack growth study, experimental fatigue life determination, and fast fracture. In order to help meet needs such as carrying out risk analysis, reliability-based design optimization, and maintenance scheduling, An et al 13 have used Bayesian technique to obtain a fatigue life prediction model for wind turbines. Their method makes use of available field data, noise, and bias in measurements on the distribution of fatigue life so that difference between designer's predicted life and field observations can be reduced.…”