# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 1 Introduction  
Anomaly detection plays an important role in the predictive maintenance of industrial systems and thus it has attracted increasing attention in the literature recently, especially in the field of machine learning. Anomalies reduce system performance and after a certain time, they may cause system malfunctions. Timely and correct detection of anomalies is of crucial importance to avoid the risk of performance loss, malfunction, and damage. Anomaly detection has been used in a wide variety of areas such as health technologies, finance and banking, military systems, and industrial systems [1]. Although anomalies arise for different reasons in different areas, the methods used for detecting them are similar, being generally based on machine learning techniques. There are various studies from different areas in the literature that are based on machine learning techniques for anomaly detection, e.g., [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. In [2], machine learning algorithms, namely neural networks, decision trees, random forests, and k-nearest neighbors are applied to the detection of anomalies for energy optimization in high rack storage systems. It is reported that random forest is superior to the other methods, with 98.50% accuracy and 96.20% recall ratios for an optimized dataset. In [3], a new adaptive method based on fuzzy clustering is proposed for the detection of data with anomalies in energy systems belonging to the steel industry. In the study, the performance of the proposed method is presented and compared with support vector machines (SVM)-based methods in terms of computing time and anomaly index. It is shown that the proposed method has better performance results compared to SVM-based methods. In [4], the detection of anomalous behaviors (cyberattacks) in industrial control systems is studied. In the study, a Boolean-function-based method, which is known aslogical analysis data (LAD), is used in the anomaly detection system. It is reported that LAD achieves an 89.10% recall ratio for attack detection. In [5], the authors present a review article in which anomaly detection for building energy consumption is discussed in detail. It is shown that anomaly detection solutions in the area are still in the initial phase and need to be promoted. In [6], an autoencoder-based method is proposed for anomaly detection in industrial time series data in order to maintain energy efficiency. The proposed method attains 92.00% precision and 64.00% recall ratios on average. In [7], an anomaly detection system using reinforcement learning is developed for detecting abnormal temperatures of a drone motor. The aim of the system is to prevent the motor from running at abnormal temperatures and to provide automatic landing ability. Simulation results showed that the system can successfully control the drone. In [8], the Gaussian mixture model (GMM)-based unsupervised learning method is applied for the detection of motor bearing failure using normal condition data. GMM learns the normal condition of the motor bearing and anomaly detection is performed by using likelihood to check the difference from the normal condition. In [9], severity detection for pitting faults in worm gearboxes is presented. In the study, a decision tree model using the distinctive features extracted from vibration data is applied in order to get more reliable results. It is shown that the area of the Poincare plot is the most dominant feature and it can be used for fault severity detection. In [10], a comparative study combining feature extraction methods with artificial neural networks (ANNs) is presented for the detection of impeller faults. Based on the results it can be concluded that the accuracy of ANNs is improved by using energy-based features and decomposed vibration signals. In [11], the anomaly detection of electricity theft cyberattacks for smart grids is studied and the performance of three autoencoder architectures is investigated.