Analysis of data from supervisory control and data acquisition (SCADA) systems to monitor wind turbine condition has attracted considerable research interest in recent years. Most approaches utilize time series data from sensors placed all over the turbine and apply methods from the machine learning (ML) domain for early failure detection (compare [1]). Besides, SCADA systems usually produce log-files which contain information about operation conditions or control events but also warning and alarm messages in case sensors measurements come close to or exceed pre-defined limits. In the condition monitoring context, this data is also of high value and has been used to filter and annotate sensor time series [2], identify message- patterns related to failures ([3], [4], [5]) and predict unplanned stoppages directly from the log-messages ([3]). However, most of these approaches use time-sequence or probability-based analysis rather than ML methods. One reason is that a ML model usually requires a numeric vector representation of fixed size as input. The challenge of finding such representations for symbol sequences of variable length is well known in the ML domain, especially in natural language processing (NLP). Within this study, we extend the Correlated Occurrence Analogue to Lexical Semantic (COALS) algorithm by incorporating temporal information (COALS-t) and apply it to SCADA log-data. We demonstrate that the method can find meaningful representations of SCADA log messages and message sequences. This enables the application of off-the-shelve ML methods. The approach will be illustrated using multiple years of operational SCADA data from several turbines. The remainder of the paper introduces the SCADA data set (section 2) and the methods being applied (section 3) before presenting and discussing the results (sections 4 and 5).