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Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques
(kassel university press, 2019)
Data-driven maintenance poses many challenges. Four very important of them, namely coping with a high dimensional and heterogeneos feature space, the highly imbalanced data sets, the Remaining Useful Lifetime (RUL) prediction of monitored parts based on short yet variable length timeseries, and already large but steadily further increasing data set size are identified. Each of the challenges is dealt with in one chapter. Novel techniques are designed, implemented, validated, and compared to existing approaches based ...