# Anomaly Detection for Hydraulic Power Units--A Case Study
## 4 Discussion
### Solution Limitations  
Potential limitations of the presented solution that are related to connectivity, data quality, and algorithm quality could be discussed. As HPU is the device that is a standalone mobile device, it has many potential locations where it can work. Therefore, some connectivity issues could arise. The most possible one is that no internet access will be available in the place where HPU operates. Hardware, for instance, the network card or antenna in MOXA UC-8100, also could be damaged. Azure IoT Edge, the technology used within the solution, is capable of storing data that should be sent to the cloud when the device has not got internet access. Data are stored on the edge computer hard disc, so in case of problems with the power supply, data will not be lost. As data are stored on a hard disk, its volume is an important aspect to mention. MOXA UC-8100 has 8 GB eMMC storage in total. After installing the operating system, software, libraries, and packages that are required for the whole solution to work properly on the edge computer, there is almost 5 GB of space left on the device. This is enough to store approximately one month of measurements with selected acquisition parameters and variable range. It is possible to use an SD card as external storage in MOXA UC-8100. However, data stored on a hard disk is safer than stored on an SD card, even if such a card is industrial grade. Even if the edge computer is capable of storing enough measurements to ensure that lack of internet access is not a problem in a broad time horizon, there could emerge connectivity issues between the edge computer and PLC. For the period when the connection is broken, data are lost. There is a gap in data that cannot be filled. The connection from sensors to the PLC input module also has the possibility of being interrupted. In such a case, there not will be a gap in data, but the quality of acquired measurements is poor. Corner cases that could emerge in such a situation are handled. There are registers in PLC that store information about the presence of the sensors. Input data to anomaly detection algorithms are filtered in a simple way by applying a threshold on data. Justification for such an approach is a known issue related to a range of sensor indications when, in case of its failure, the sensor outputs a minimal or maximal analog value that corresponds to too large or too small indications. Corrupted data from two corner cases described above is therefore not considered for further analysis. However, there is no mechanism implemented that ensures all issues with sensors that could emerge. Therefore, data corruption, however unlikely, is always possible even if measurements do not seem suspicious. To address potential problems with reading from sensors, in the literature in the paper [80], the algorithm related to machine condition monitoring that takes data from malfunctioning sensors into account is presented. Another interesting example was presented in the publication [81], where the authors developed an algorithm called the adjacent information recovery (AIR) filter, which handles incomplete data issues in fault detection systems. The mentioned methods solve a variety of issues with incomplete data; however, the adaptation of the above algorithms into the solution presented in this article will consume time that, at this stage of a project, is not available. Models tests that were carried out during algorithm development helped to train and tune algorithms in a way that they did not raise useless alarms (false positives) or that algorithms missed alarms (false negatives). The amount of data from tests is not sufficient to cover all possible conditions and working regimes of HPU. For sustainable industry, as stated in the publication [82], both alerting on the most severe anomalies and minimizing false positives are critical. From that point of view, the number of false negatives increases; however, this is a common compromise for anomaly detection tasks in industrial applications [83].