# Anomaly Detection for Hydraulic Power Units--A Case Study
### Literature Review  
To compare the solutions developed in the article, a literature review is mandatory. The tools, concepts, and techniques that have been applied and described so far in the literature could be useful to point out the advantages and disadvantages of the presented approach. What is more, the examination of the state of the art allows for indicating innovations that this article brings into the domain. More generally, not only related to hydraulic units, anomaly detection in industry was described in the survey by [12]. According to the categorization presented in that paper, the work within this article could be described as statistical/machine learning--constructive--point--online. In reference to the same survey, this project by application fits into the \"machine condition monitoring\" category of anomaly detection. An example of a case study involving a statistical anomaly detection method also described within the article is presented in the work [13]. The authors used the interquartile range (IQR) [2] method to determine anomalies for e-coating plants. IQR was considered a better tool than the Isolation Forest and Elliptic Envelope algorithms that were also taken into account. Many anomaly detection algorithms and use cases were mentioned and classified in the context of IoT in the publication [14]. According to the paper, sources of the anomaly could be, among others, errors and noise while collecting data and anomalous events during the  
Figure 1: Photo of the hydraulic power unit that is the subject of the project. operation of the device. The authors proposed grouping anomalies into three groups: point outliers, contextual outliers, and collective outliers. Regarding the hydraulic domain, in the article [15], an online leakage detection system was developed that was also based on IQR value. The acquired 1-year monitoring datasets contain four gradual leakage events and two burst events. The presented results showed that the proposed method has successfully detected all leakage events with a short detection time and did not generate any false alarms in a year, which showed a promising future for this method. The second modeling approach used in the paper involves black-box models, which are capable of capturing more complex dependencies in data; however, it is harder than in the case of the statistical approach to precisely describe in detail how output hypotheses were developed by the model. In the literature in an industrial context, HVAC devices were the subject of a research study presented in the publication [16]. The authors used the One Class SVM model with success to detect anomalies by first determining data from a device operating in nominal conditions. Afterwards, they changed the operating point and evaluated the learned model. The approach presented in [17] describes condition monitoring of complex hydraulic systems. The monitored elements were coolers, valves, internal pumps, and hydraulic accumulators. The Deep Neural Network (DNN) model was explained with the use of Deep SHapley Additive exPlanations (DeepSHAP) to determine what the most important factors that influence the final output of a model are. The accuracy metric that describes the quality of fault detection performed within the paper for monitored hydraulic elements were as follows: cooler-99.87%, valve-99.60%, internal pump leakage-99.09%, and stage flag (94.17%), with the exception of the hydraulic accumulator conditions (88.60%). Very good performance in terms of F1-score was achieved by the One Class SVM model presented in the same paper. The F1-score for the hydraulic system that was the subject of the research study was 99.68%. The anomaly detection approach developed for the hydraulic system was presented in the publication [18]. The dataset used within the paper comes from a real-world installation in Singapore. It emerged as a business need for research. In 2018, on a yearly basis, an average of 4.6 leak events per 100 km were reported.