A workflow for the specific case of process monitoring data which contains cycles repeating over a long time is presented. The method relies on predefined features ideally used as health-indicators which are aggregated over the cycles. Anomaly detection, HI-classification and RUL-prediction is then performed on the time series of aggregated values indexed by the cycles. For all three tasks, modern machine learning models are used to assist their completion. Apart from the predictions of health state