# Machinery Fault Signal Detection with Deep One-Class Classification
###### Abstract  
Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal class. For more accurate one-class classification, signal data have been used recently because the signal data directly reflect the condition of the machinery system. To analyze the machinery condition effectively with the signal data, features of signals should be extracted, and then, the one-class classifier is constructed with the features. However, features separately extracted from one-class classification might not be optimized for the fault detection tasks, and thus, it leads to unsatisfactory performance. To address this problem, deep one-class classification methods can be used because the neural network structures can generate the features specialized to fault detection tasks through the end-to-end learning manner. In this study, we conducted a comprehensive experimental study with various fault signal datasets. The experimental results demonstrated that the deep support vector data description model, which is one of the most prominent deep one-class classification methods, outperforms its competitors and traditional methods. condition-based maintenance; deep one-class classification; deep support vector data description; fault signal detection; time series signal +
Footnote †: journal: Medical  
is obvious that appropriately preprocessing the raw sensor data and accurately detecting the fault symptoms are fundamental prerequisites for a successful CBM strategy [9; 10]. Therefore, the main purpose of this study is to propose a fault detection framework with deep neural network structures in order to implement a more efficient CBM strategy. The deep neural network structures are specialized to the feature engineering for given learning tasks [11; 12]. Hence, the deep neural network-based approaches might show superior fault detection performance. Most of the previous studies on machinery fault detection have used the sensor data obtained from both normal operating and fault status. However, the data from the fault status are not available in general because the fault rarely occurs in many real situations [13; 14]. In addition, the operating status label information is not easy to obtain because it requires expensive analytical costs on machinery operating status. Therefore, in this study, we focus on the unsupervised deep neural network-based fault detection methods, which only use the sensor data obtained from normal operating status. In recent years, various time-series signal data, including vibration, acoustic, and thermometric signals, have been widely used for fault detection [15; 16]. These time-series signal data explicitly reflect machinery operating status, and it helps to more accurately detect the fault symptoms. Besides, owing to the rapid development of sensing and data storage techniques, a large amount of time-series signal data can be easily collected in real-time [8; 17]. Therefore, utilizing these time-series signal data for fault detection has been spotlighted by a number of machinery operators. Then, a fault detection model can be constructed using one-class classification (OCC) methods. The OCC methods construct a decision boundary solely using time-series signal data obtained from only normal status (generally referred to as target signal), and the decision boundary finally determines whether a newly collected time-series signal was generated from normal status or not [18; 19; 20]. If the time-series signal is located outside the decision boundary, it is rejected as a fault signal, which is considered that the machinery fault might occur, and appropriate actions should be rapidly taken.