Condition monitoring is one of the most prominent industrial use cases for machine learning today. As condition monitoring applications are commonly developed using static training datasets, their long-term performance is vulnerable to concept drift in the form of time-dependent changes in environmental and operating conditions as well as data quality problems or sensor drift. When the data distribution changes, machine learning models can fail catastrophically. We show that two-sample tests of homogeneity, which form the basis of most of the available concept drift detection strategies, fail in this domain, as the live data is highly correlated and does not follow the assumption of being independent and identically distributed (i.i.d.) that is often made in academia. We propose a novel drift detection approach calledLocalized Reference Drift Detection (LRDD) to address this challenge by refining the reference set for the two-sample tests. We demonstrate the performance of the proposed approach in a preliminary evaluation on a tool condition monitoring case study.