# Anomaly Detection for Hydraulic Power Units--A Case Study
### Literature Review
A model that is capable of signalizing anomaly 3 days before and localized with near 400 m accuracy with 80% F1-score has been developed. More than 3300 data-driven models were trained for optimizing the model performance to achieve the desired quality of modeling. An anomaly detection system for HPU specifically was developed and presented in the paper [19]. The authors used several deep learning models such as the autoencoder, one-dimensional convolutional neural network, and long-short-term-memory network. The best model (1 DCNN) achieved a distinction between normal data and data including anomalies with 94% accuracy in an industrial dataset enhanced with artificial anomalies. In the context of the article, the use of expert knowledge for feature engineering is crucial. In the literature so far, the use of expert knowledge was applied successfully in the publication [20] for the spot welding process. In the described use case, the wear count threshold was set by domain experts, and this value determined the workflow of the anomaly detection algorithm. Another example of incorporating domain experts' knowledge for feature engineering into industry was described in the article [21]. The subject of the article was a bitumen oven. As the authors stated, the description of the operating point and the ranking of the inputs were important for achieving satisfying quality in the models for predicting the surface temperature of the oven. The output of the model was used to enable the optimization of process parameters. However, in the literature, there is no described use of domain experts' knowledge about hydraulic power units in the context of feature engineering for anomaly detection tasks, which is presented in this article. The feature selection process, which is also part of the presented anomaly detection solution, was described in the literature using different approaches. One of the simple ones, which involves the Pearson Correlation Coefficient (PCC), was presented in the article [22] for the purpose of feature engineering of sensorized stamping presses. A use case closer to the subject of the herein article is presented in [23], where the authors used PCC to perform feature engineering for hydraulic components anomaly detection. There are also more complicated modern approaches involving neural networks. As an example of usage, autoencoders [24] or convolutional neural networks [25] could be provided. In addition, there is also the possibility of mixing together both PCC and neural networks in the feature engineering process as presented in the publication [26]. In the context of designing IoT solutions, in the publication [27], several trends and guidelines were provided. The authors mentioned that due to limited edge device resources, instead of implementing a whole TCP/IP stack based on the Open Systems Interconnection (OSI) model, a lightweight IP stack is normally implemented. Another important aspect to keep in mind while developing or projecting IoT solutions is the energy consumption of the device because that energy consumption primarily regulates the implementation of suitable protocols and standards in IoT devices. The study reveals that, in the context of security, [PERSON] et al. [28] proposed a lightweight encryption scheme that enables fast hash-keys-based encryption for IoT modules in the perception layer. The proposed scheme can mitigate security risks by only allowing communication between authenticated IoT devices. Considering current issues and trends in the domain of anomaly detection for hydraulic power units, some open research problems can be named. Researchers try to explain black-box models so that the influence of input variables on the final output can be measured. The quality of the algorithms developed so far is very good. However, there are many more possibilities to combine existing algorithms, neural networks, and statistical approaches.