# Anomaly Detection for Hydraulic Power Units--A Case Study
## 6 Conclusions and Final Thoughts  
This article presents a method of combining analytical solutions, edge devices, and SCADA. The subject of the project was a hydraulic power unit (HPU), a high-pressure hydraulic device that fits into the water as a tool concept. Within the project, integration of PLC, edge computer, and Microsoft Azure Cloud was performed. Communication with PLC was performed using the Modbus protocol and communication with Azure IoTHub was established using the MQTT protocol. The code of programs for data acquisition and sending to the cloud was implemented in Python and enclosed within Docker containers with the use of Azure IoTEdge technology. Moreover, developing analytical hypotheses was implemented using Python; however, algorithms that determined whether anomaly occurred or not were executed serverless using Azure Functions and Azure Storage.  
Several conclusions could be derived from the work that was carried out in the article. Everything that is presented came from the industrial project and is used in the production of hydraulic power units. The approach to data acquisition, analysis, and presentation could be a reference for other IoT projects. The algorithms described give an insight into what can be important in the context of predictive maintenance and machine monitoring for HPUs. The cost analysis of Azure cloud services and resources used in project implementation was performed, so that the cost of deployment of the project at scale could be more easily estimated.  
Innovations that come from the project are algorithms and models designed and developed for a concrete model of HPU. Furthermore, the use of algorithms described in scientific literature distinguishes the project from other commercially implemented solutions. In the literature, there are no presented use cases that involve the IQR statistical measure for hydraulic power units specifically. The use of Microsoft IoTEdge and other components of the Azure Cloud presented in the article was not previously described in the literature in the context of commercial implementation. Furthermore, the method of deploying algorithms, the possibility of rolling out the solution at scale, and the flexibility of the solution to be implemented both in the cloud and on-premise variants are aspects that together are added value that comes from the realization of the described project.  
In the current phase of the project, PONAR Wadowice S.A. profits from the ability to monitor and diagnose HPU remotely via web SCADA. Therefore, the goal of the project has been fulfilled. It is important from a business point of view, as a result of the project, that PONAR Wadowice can sell HPU as an innovative product. The business  
Figure 12: IoTEdge ecosystem enriched in ML model deployed as Docker container at scale.  
value and effectiveness of the solution are worth inspecting again after gathering more production data.  
Conceptualization, methodology, software, P.F.; writing--original draft preparation, P.F.; writing--review and editing, A.C.; Conceptualization, P.R. All authors have read and agreed to the published version of the manuscript.  
The research is co-financed under the Polish Ministry of Science and Higher Education Program \"Doktorat wdrozienowy\".  
Restrictions apply to the availability of these data. Data was obtained from REDNT S.A. and PONAR WADOWICE S.A. and are available from REDNT S.A. and PONAR WADOWICE S.A. with the permission of REDNT S.A. and PONAR WADOWICE S.A.  
Part of the content of the article was presented on 18 June 2021 at the conference \"DSS 2021 ML Edition\" ([[https://ml.dssconf.pl/](https://ml.dssconf.pl/)]([https://ml.dssconf.pl/](https://ml.dssconf.pl/)), accessed on 23 December 2021). Project was implemented in cooperation with PONAR Wadowice S.A. [91] and REDNT S.A. [92].  
The authors declare no conflict of interest.