# Anomaly Detection for Hydraulic Power Units--A Case Study
###### Abstract  
This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow. predictive maintenance; internet of things; hydraulic power units; machine learning; anomaly detection; cloud computing; Microsoft Azure Cloud +
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through the use of new digital technologies, data resources, and the provision of networked communication between machines, devices, and people. The driving factor behind the transformation to Industry 4.0 is the increasingly individualized needs of customers and the growing trend of personalization of products and services [5; 6]. Within Industry 4.0, the Cyber-Physical System (CPS) plays a crucial role as an integrator, and its success depends on the smart management of interconnected systems between its physical components and computational capabilities, utilizing state-of-the-art technology in both cyber and physical worlds [7]. The Industrial Internet of Things (IIoT) refers to the application of the Internet of Things IoT in the industrial field. The IIoT integrates the industrial internet, next-generation information technology, and industrial systems. It can effectively reduce production costs without compromising production efficiency. The IIoT is considered the foundation of the future industrial system. Along with the increased productivity, the IIoT generates massive, high-dimensional, and heterogeneous real-time data [8]. This poses challenges associated with the Big Data concept--the enormous volume of data that cannot be managed and processed by conventional data management methods [9]. Processing this enormous volume of data falls into Data Mining. As a definition, it is the analysis of large-sized groups of observed data to search for potentially summarized forms of data that are more understandable and useful to the user. With the aim of extracting or discovering useful and exploitable knowledge from a large collection of data, it helps explore hidden facts, knowledge, and unexpected models [10]. Examples of CPS, IIoT, Big Data, and Data Mining are presented in the article within a commercial use-case regarding anomaly detection systems for hydraulic power units. The subject of the project was a single hydraulic power unit (HPU). However, the developed methodology could be propagated on more devices of the same kind. Propagation on exactly the same hydraulic power unit model devices would be easy. However, in the case of implementation on different models of hydraulic power units, some actions have to be carried out. This means that to perform anomaly detection tasks properly, data from new models of devices should be acquired and analyzed in the same way as it would be for the hydraulic power units described in this article. This approach is not scalable within the same product line, as the models of HPUs manufactured by PONAR within the same product line differ not only in operating parameters (e.g., power) but also in design, which prevents full scaling of the approach.