The sensor monitoring systems of today can detect anomalous behaviour & derive their underlying causes by either using expert-driven rules or by data-driven machine learning models. Expert-driven approaches require much human involvement to operate in environments providing the expert information on the fly. In contrast, data-driven approaches are more adaptive to specific changes but require large amounts of data to generalise well & have difficulties to interpret or derive the causes. In the end, a trade-off must be made between the non-adaptive approaches requiring a lot of human effort or the less interpretable models generating floods of alarms. To resolve these problems, the goal of this research is to autonomously incorporate the expert knowledge from the application domain into multiple parts of the data-driven learning process: - Expert knowledge will be used inside the anomaly detection tools, to reduce the number of falsely & underpredicted anomalies. - Cause analysing methods will derive the underlying reason for the detected unwanted behaviour by using a combination of interpretable detection models & the available expert knowledge. - Domain expertise together with the sensor observations will be used to profile the normal behaviour, resulting in a better understanding of the given data. Combining these three parts will result in an interpretable & adaptive sensor monitor tool, evaluated within the predictive maintenance & healthcare domain.