# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 2 Related Works
### Feature Extraction for Analyzing the Signals
As for the feature extraction, in several previous works, manually defined features through both the time-domain and frequency-domain analysis on the raw signals have been used. In the time-domain analysis, simple statistics, including kurtosis, skewness, and crest factor, can be used as features, and those statistics from the vibration signals have been used. Meanwhile, in frequency-domain analysis, frequency-related features, including mean frequency (MF), root mean square frequency (RMSF), and frequency center (FC), are also derived from the power spectrum of the vibration signal for diagnosing the states of rotating machinery. In addition to these manually defined features, feature learning methods also have been used to extract the features from raw signals. In the feature learning methods, individual values recorded at each time point or frequency component of raw signal data are treated as variables. For this reason, a raw signal collected at a high sampling rate can be regarded as high-dimensional data, and thus, the feature learning methods entail dimensionality reduction to summarize those high-dimensional signal data into small-sized features. As shown in Table 1, PCA, one of the most representative unsupervised dimensionality reduction techniques, has been successfully used for extracting features from raw signals in FDD fields. The PCA attempts to find the optimal small dimensions to maintain as much of the covariance structures between original variables as possible. By doing so, the smaller dimensions obtained from the PCA can summarize much information on the original raw signals. However, these two feature extraction schemes are separately conducted with FDD processes, and hence, the extracted features might not be specialized to FDD tasks. Moreover, the PCA-based feature extraction techniques are linear methods, and they cannot consider the nonlinearity of input signals. Recently, deep neural network (DNN) structures also have been used for feature learning. The DNN structures have a number of hidden layers between input and output layers, and the weights between layers can be trained by minimizing a loss function for given learning tasks. Using optimal weights, several useful features for the learning task can be generated as they pass through hidden layers. Those processes, referred to as the end-to-end manner, guarantee that the extracted features are specialized to the given learning tasks. Thus, the DNN structures can be successfully used to extract meaningful features from the raw signals. Because of this advantage, the DNN structures have received considerable attention in recent studies. Please note that most of the existing DNN-based FDD studies are based on supervised approaches which fully rely on the class label information on the operating state or fault types. However, those approaches might not be available in many real industrial fields, because rotating machinery faults have rarely occurred compared to normal operation. Therefore, the label information related to machinery faults is not often available in training procedures. To cope with the absence of fault label information in training procedures, several recent studies have applied unsupervised learning approaches using stacked autoencoder (SAE), convolutional autoencoder (CAE), and deep belief network (DBN) structures to draw meaningful features from vibration signals without fault label information. However, most of the previous works employed the unsupervised learning approaches that only used single domain-represented signals, which are limited to accommodating various aspects of signals.