Background. One of the challenges about smart control valves system is processing and analyzing sensors data to extract useful information. These types of information can be used to detect the deviating behaviors which can be an indication of faults and issues in the system. Outlier detection is a process in which we try to find these deviating behaviors that occur in the system.Objectives. First, perform a literature review to get an insight about the machine learning (ML) and data mining (DM) techniques that can be applied to extract patternfrom time-series data. Next, model the operating behaviors of the control valve system using appropriate machine learning and data mining techniques. Finally,evaluate the proposed behavioral models on real world data.Methods. To have a better understanding of the different ML and DM techniques for extracting patterns from time-series data and fault detection and diagnosis of building systems, literature review is conducted. Later on, an unsupervised learning approach is proposed for modeling the typical operating behaviors and detecting the deviating operating behaviors of the control valve system. Additionally, the proposed method provides supplementary information for domain experts to help them in their analysis.Results. The outcome from modeling and monitoring the operating behaviors ofthe control valve system are analyzed. The evaluation of the results by the domain experts indicates that the method is capable of detecting deviating or unseen operating behaviors of the system. Moreover, the proposed method provides additional useful information to have a better understanding of the obtained results.Conclusions. The main goal in this study was achieved by proposing a method that can model the typical operating behaviors of the control valve system. The generated model can be used to monitor the newly arrived daily measurements and detect the deviating or unseen operating behaviors of the control valve system. Also, it provides supplementary information that can help domain experts to facilitate and reduce the time of analysis.