Prognostic methodologies have found increasing use the last decade and provide a platform for remaining useful life (RUL) predictions of engineering systems utilizing condition monitoring data. Of particular interest is the reliable RUL prediction of engineering assets that either underperform or outperform due to unexpected phenomena that might occur during the operational life. These assets are often referred as outliers and the prediction of their RUL is a challenging task. The challenge is to accurately predict the RUL of an outlier without taking into account outlier’s condition monitoring data in the training process but just in the testing process. As a result, the lifetime of the testing asset is shorter (left outlier) or longer (right outlier) than the training process’ lifetimes.
This study addresses this challenge by proposing a new adaptive model; the Similarity Learning Hidden Semi Markov Model (SLHSMM), which is an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). The SLHSMM uses a similarity function, such as Minkowski distances, in order firstly to quantify the similarity between the testing asset and each training asset and secondly to adapt the trained parameters of the NHHSMM. To demonstrate the effectiveness of the proposed adaptive methodology, composite structures have been used as a validation engineering asset. In particular, the training data set consists of strain data collected from open-hole carbon–epoxy specimens, which were subjected to fatigue loading only, while the testing data set consists of strain data collected from specimens that were subjected to fatigue and in-situ impact loading, which can be considered as an unexpected phenomenon and unseen event regarding the training process.
Utilizing the aforementioned strain data the SLHSMM RUL predictions and the NHHSMM RUL predictions were compared, so as to verify that the SLHSMM provides better prognostics than the NHHSMM. SLHSMM provides better predictions in comparison to the NHHSMM for all the test cases, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data to the prognostics course.
How to Cite
remaining useful life, prognostics, adaptive prognostics, outlier analysis
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