  Machine failures decrease up-time and can lead to extra repair costs or even
to human casualties and environmental pollution. Recent condition monitoring
techniques use artificial intelligence in an effort to avoid time-consuming
manual analysis and handcrafted feature extraction. Many of these only analyze
a single machine and require a large historical data set. In practice, this can
be difficult and expensive to collect. However, some industrial condition
monitoring applications involve a fleet of similar operating machines. In most
of these applications, it is safe to assume healthy conditions for the majority
of machines. Deviating machine behavior is then an indicator for a machine
fault. This work proposes an unsupervised, generic, anomaly detection framework
for fleet-based condition monitoring. It uses generic building blocks and
offers three key advantages. First, a historical data set is not required due
to online fleet-based comparisons. Second, it allows incorporating domain
expertise by user-defined comparison measures. Finally, contrary to most
black-box artificial intelligence techniques, easy interpretability allows a
domain expert to validate the predictions made by the framework. Two use-cases
on an electrical machine fleet demonstrate the applicability of the framework
to detect a voltage unbalance by means of electrical and vibration signatures.
