Preprocessing and Modeling of Radial Fan Data for Health State Prediction
F. Holzinger, M. Kommenda - Preprocessing and Modeling of Radial Fan Data for Health State Prediction - Computer Aided Systems Theory – EUROCAST 2019, Las Palmas, Gran Canaria, Spain, 2019
Monitoring critical components of systems is a crucial step towards failure safety. Aﬀordable sensors are available and the industry is in the process of introducing and extending monitoring solutions to improve product quality. Often, no expertise of how much data is required for a certain task (e.g. monitoring) exists. Especially in vital machinery, a trend to exaggerated sensors may be noticed, both in quality and in quantity. This often results in an excessive generation of data, which should be transferred, processed and stored nonetheless. In a previous case study, several sensors have been mounted on a healthy radial fan, which was later artiﬁcially damaged. The gathered data was used for modeling (and therefore monitoring) a healthy state. The models were evaluated on a dataset created by using a faulty impeller. This paper focuses on the reduction of this data through downsampling and binning. Diﬀerent models are created with linear regression and random forest regression and the resulting diﬀerence in quality is discussed.