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Field
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conceptualized as a specialized form of anomaly detection. Specifically, the objective is to identify anomalies that evolve gradually and to forecast the time-to-failure with sufficient accuracy. Consequently
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estimation of RUL can be conceptualized as a specialized form of anomaly detection. Specifically, the objective is to identify anomalies that evolve gradually and to forecast the time-to-failure with
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term