# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 2 Hydraulic System Fault Analysis  
We use the hydraulic system condition monitoring dataset available from the UC Irvine Machine Learning Repository [15], which is comprised of the primary and secondary circuits shown in Figure 1. This dataset consists of 17 sensor measurements with the details of these sensor specifications listed in Table 1. The components of the hydraulic sensor include 14 physical sensors, including six pressure sensors (PS1-PS6), four temperature sensors (TS1-TS4), two volume flow sensors (FS1-FS2), a motor power sensor (EPS1), a vibration sensor (VS1), and three virtual sensors of a computed values-efficiency factor sensor (SE), a cooling efficiency sensor (CE), and a cooling power virtual sensor (CP). In addition, the hydraulic circuit maintains other sensors, such as oil parameter monitoring (COPS) and oil particle contamination (CS and MCS). Each sensor conducts measurements during a load cycle of 60 s, with sampling rates or frequencies ranging from 1 Hz to 100 Hz. The dataset consists of 2205 load cycles or samples, with each sample having a component state label corresponding to the fault condition of the components. Table 2 describes the detailed taxonomy of the fault states for the components, which includes four fault component targets of Cooler, indicating a cooling power fault,  
industrial condition monitoring. [PERSON] et al. [16] proposed an edge-based fault detection using an LSTM model in an industrial robot manipulator that incorporated vibration, temperature sensors, and the use of one edge device attached to the machine and pressure sensors. [PERSON] et al. [17] proposed edge-based fault detection using density-based spatial clustering and a random forest algorithm for an automobile parts factory that employed an edge model on each workstation of the assembly line. [PERSON] et al. [18] proposed edge-based visual defect detection using a convolutional neural network (CNN) model in a tile production factory that deployed multiple cameras to capture visual information of the products [19]. These data were then sent to an edge node to inspect potential defects. Even with these approaches, there remain few studies on fault detection within the context of applications in edge computing. Several works exist that used the same data sets as in this study. [PERSON] et al. [15; 20] convert the time domain data into frequency domain using fast fourier transform, and generate statistical features, such as the slope of the linear fit, median, variance, skewness, the position of the maximum value, and kurtosis [21]. They then calculated features for fault label correlation and selected the \(n\) features by ranking or sorting the correlation (CS). Finally, this approach applied linear discriminant analysis [22] for the fault classification. [PERSON] et al. [23] also utilized statistical features of frequency domain data, such as mean, skewness, and kurtosis, and applied XGBoost [24] to define feature importance (XFI) and select half of the highest correlations along with a deep neural network for the classification model. In [25], the authors proposed a dimensional reduction approach with principal component analysis (PCA) [26] to transform the raw features to a fewer number of principal component features, and then classify the faults using XGBoost. [PERSON] et al. [27] and [PERSON] et al. [28] proposed a CNN [29] as the classification model into which they directly fed the raw data because CNNs can extract features. These previous approaches include several drawbacks. First, none considered edge computing for application to hydraulic system fault detection that supports real-time detection and reduces transmission costs. Second, such statistical feature extraction, PCA, and other feature engineering methods are not suitable for real-time detection. These techniques must be applied to each new incoming sample, thereby consuming more time and computation power. Thus, directly using the raw data with less feature engineering is preferred.