# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 1 Introduction  
Hydraulic systems are utilized in several industrial applications, including manufacturing, automobiles, and heavy machinery [1; 2; 3; 4; 5]. Monitoring the condition of hydraulic equipment is essential, as it can maintain high productivity and reduce the costs of system processes [6]. Three challenges to the formulation of fault detection in a hydraulic system exist. First, conducting automated and real-time fault detection without human intervention is difficult because of the time-sensitive requirements to maintain proper system functionality [7]. Second, industrial sensor data are now more abundant in sample quantity and dimension [8]. A hydraulic system operates with multivariate time-series data obtained from multiple sensors, so identifying errors is challenging due to complex nonlinear relationships between data. Third, real-world industrial sensors are often located within noisy environments, so the data collected from these tend to be noisy and unreliable. Noise occurs typically because of inaccuracies in sensor configurations or interference from the external environment [9]. Because operational decisions are made based on these sensor readings, the data must be more reliable. Therefore, a solution that addresses these challenges of fault detection in hydraulic systemsmust include establishing a real-time detection process, learning multivariate time-series sensor data, and handling noisy data. The concept of Industry 4.0 [10] is proposed as the current state-of-the-art among IT and manufacturing that offers enhancements in product quality, real-time decision-making, and integrated systems. Advanced technologies, such as the cloud, internet of things, and artificial intelligence, are integrated into modern manufacturing [11; 12]. The cloud includes the drawback of high response times due to the long-distance transmission of massive data volumes. However, real-time responses are essential for fault detection systems. Edge computing is designed to overcome this challenge by offering computational resources near the data source resulting in decreased latency and transmission costs [13]. Cooperation between cloud and edge servers must occur for real-time fault detection in hydraulic systems [14]. For example, the cloud server can utilize offline learning, such as selecting the important data to transfer and performing model training, while the real-time intelligent service of fault detection is executed on the edge server. We propose a genetic algorithm (GA)-based feature selection method that considers feature correlations and redundancies. Sensor feature selection is incorporated to improve the fault detection model performance while simultaneously utilizing fewer data by defining which sensor data should be sent to reduce packet transmission. A GA offers an efficient solution for search without pre-training with any domain knowledge. For the fault detection model, we employ a stacked long short-term memory autoencoder (LSTM-AE) to extract features from the sequence data automatically, which performs efficiently on latent features from clean or noisy data. Finally, the pre-trained encoder is merged and re-trained with a dense layer and Softmax classifier to perform the fault detection. The main contributions of this study include the following:  
* We propose cooperation between cloud and edge servers to support a real-time fault detection system. The cloud performs feature selection and offline learning, and the edge computes online detection near the data source, which together reduces latency and transmission costs. * We propose a GA-based feature selection that considers correlation and redundancy to ensure the selection of features that are most important and not redundant for learning. * We propose an LSTM-AE as the fault detection model to learn the temporal relations in the time-series data and extract latent features from noisy data. In the remainder of this paper, we explain the hydraulic system fault analysis in Section 2.