[PERSON] analyzed the effects of piston scratches on engine vibration using short-time Fourier transform (STFT) and continuous wavelet transform (CWT) [34]. [PERSON] detected engine misfire faults by analyzing the instantaneous angular speed of the engine in the frequency domain [40]. [PERSON] proposed a fault detection method for motor bearing faults using a machine learning technique based on multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing map (SOM) networks [41]. [PERSON] proposed a machine learning based fault detection method for battery sensors of HEV that used one-class support vector machine (SVM) and k-means clustering together [33]. [PERSON] detected faults of sensor data in a controller area network (CAN) bus using machine learning with the features by extracting features using cause and effect rules [36]. [PERSON] conducted fault diagnosis for an EV battery using variational mode decomposition and clustering [42]. In particular, in [44], the authors diagnosed faults of vehicle powertrain by using mathematical models and a neuro-fuzzy network together. So far, studies aimed at detecting fault or anomalies on vehicle powertrains have been conducted mainly on individual parts such as the motor, battery, and transmission. Little research has been conducted on detecting anomalies in the system operation level of the powertrain control. In [6; 22; 26], the authors conducted studies to detect anomalies by examining vehicle data trends. In [10; 36], the authors detected relatively simple anomalies through DTC signals or a tendency to increase/decrease between data. However, there is a limit to these applications to the verification of complicated control functions. In [32], the authors also studied to detect anomalies by examining vehicle data trends with machine learning. The classifier resulted in relatively low precision, and the precision results were different according to the test data. Therefore, this paper studied the anomaly detection method for complicated HEV powertrain control functions. In particular, we examined the methods for detecting anomalous lies in the engine clutch engagement/disengagement process required for EV\(\leftrightarrow\)HEV mode transitions, which is a crucial function of TMED type HEVs. We used data-driven methods to make it easy to apply to various vehicle data in the future. Additionally, previous studies' rule-based methods and mathematical model-based methods have limitations in applying them to target control function. The rule-based techniques are difficult to apply to complex control functions because they use simple rules generally. In addition, it is not easy to construct a mathematical model for the engine clutch engagement/disengagement process. As noted earlier, little research has been conducted on detecting anomalies in the system operation level of the powertrain control. Therefore, we used the basic and most widely used learning architecture. We used multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) to train the models for anomaly detection and compared the trained models. MLP is the most basic neural network architecture, and CNN and LSTM are the most widely used learning architectures recently. To investigate the performance of the trained model for actual vehicle data, we used real vehicle test data. As a result of the study, we found that the one-class classification method is the most effective. This paper is organized as follows. Section 2 introduces the structure of TMED type HEVs and the engine clutch engagement/disengagement process in more detail. Section 3 describes the basic data preprocessing for model training and testing, and Section 4 explains the anomaly detection model training methods. Section 5 shows the test results for the trained models, and the conclusions are described in Section 6.