# Machinery Fault Signal Detection with Deep One-Class Classification
###### Abstract
Although the OCC methods have shown satisfactory performance in fault signal detection, they require an additional procedure that derives meaningful features from raw time-series signal data. This additional procedure is termed feature extraction. The feature extraction attempts to generate several latent variables summarizing intrinsic properties of raw time-series signals. In most previous studies, manually created features (e.g., root mean square (RMS), kurtosis, and crest factor (CF)) have been widely utilized [21; 22; 23]. On the other hand, traditional feature learning techniques, including principal component analysis (PCA) [24] and kernel principal component analysis (KPCA) [25], have also been successfully used as feature extraction methods. The raw time-series signals are recorded at high sampling resolution and can be treated as high-dimensional data because individual values of the signal recorded at each time point can be regarded as variables. Thus, these traditional feature learning techniques entail dimensionality reduction to summarize the raw time-series signals of high dimensionality into smaller useful features. In spite of its simplicity, the manually crafted features simply summarize information of raw time-series signal data into a small number of values, and thus, it is difficult to reflect various characteristics of complex and noisy signals having nonstationary and nonlinear patterns [26; 27]. For this reason, the manual feature creation may not be suitable for fault signal detection tasks. In addition, several traditional feature-learning techniques cannot accommodate the nonlinearity of target time-series signals, and kernel-based methods tend to be sensitive to the kernel function settings [28]. Finally, the feature creation or feature learning procedure is separately performed before fault detection model construction, and hence, the generated features may not be specialized for fault detection tasks [29]. To address these limitations, in recent years, deep neural network structures have been incorporated into OCC methods. In the deep neural network-based OCC methods (referred to as deep OCC methods), the feature extraction procedure is simultaneously performed with fault signal detection tasks in an end-to-end manner [11; 12]. Thus, the deep neural network is basically designed to generate features optimized to a specified loss function of its corresponding task. Owing to the end-to-end manner's advantage, deep neural network structures have been successfully used in OCC methods in recent years. Among various OCC methods, the boundary-based OCC methods (e.g., support vector data description (SVDD [30]) and one-class support vector machine (OCSVM [31])) have been the most widely combined with the deep neural network because their objective function can be easily reformulated for a loss function of the deep neural network. In the boundary-based methods, the objective function is formulated to construct a compact hypersphere enclosing the target class, and the hyperspheres can be used as a decision boundary to discriminate the fault signals from target signals. By doing so, optimized features through the deep neural network structure help build more sophisticated decision boundaries by accommodating the inherent patterns of target signals. The following are the main contributions of this study:  
* The deep OCC methods can achieve superior fault detection performance, although raw time-series signals are directly used as input data. In general, for more effectively analyzing the time-series signals, signal processing techniques (e.g., short-time Fourier transform (STFT [32; 33]) or wavelet transform [34]) are used to transform the raw signals. However, these signal-processing techniques require additional user-specified hyperparameters, which should be carefully determined. In contrast, the deep OCC methods do not need any signal processing techniques. This implies the efficiency of the deep OCC methods in handling the raw time-series signal data.