Abstract
Modern aircraft health estimation methods are too strict for predictive maintenance estimation and require a high degree of cleanliness of the data. It is preferable to develop thresholds for maintenance personnel to arrange repairs, especially for batches of equipment. The data collected in real scenes are usually incomplete, contain noise, and occupy a large proportion. Moreover, existing methods generally delete “NaN” data directly, which is actually a waste and unreasonable. Aiming at the problem, this study proposes using a survival analysis method for fault prediction. Incomplete data can also be used; they are usually removed in traditional machine learning methods, even in deep learning methods. This will greatly improve the efficiency of data usage. A maintenance evaluation model system was developed to conduct all experiments, which showed what. The results are close to those of the time-series analysis, which indicates that there is much room for improvement.
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Gu, J., Liu, K., Chen, J., Sun, T. (2022). Predictive Maintenance Estimation of Aircraft Health with Survival Analysis. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_32
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