A general anomaly detection framework for fleet-based condition monitoring of machines

•A framework is proposed for fleet-based condition monitoring.•Online fleet-based comparisons avoid the need for a historical data set.•It is easy for a domain expert to interact with this framework.•A fleet of electrical drivetrains is used to experimentally validate the framework.•The framework is benchmarked against a classic signal processing approach.