Predictive maintenance based on event-log analysisKljun, Maša (Avtor) Demšar, Jure (Mentor) D’Alconzo, Alessandro (Komentor) predictive maintenancemachine logsmaintenance logstime seriesThe success of manufacturing companies is highly reliant on the performance of their machinery. Unplanned downtimes of the machines may cause severe profit loss, so it is important to prevent such events. One of the ways of achieving an undisturbed manufacturing process is with predictive maintenance, which allows factories to switch from reactive to proactive action taking by predicting a machine failure before it even happens. Predictive maintenance can be performed by utilizing sensor measurements, however, there are a lot of costs associated with installing and maintaining new sensors. A promising and more cost-friendly alternative is predictive maintenance based on machine logs. In this thesis, to tackle the problem of failure prediction, we performed a thorough literature review and found two suitable state-of-the-art approaches that utilize machine logs. We implemented both approaches and made several improvements. To better assess the quality of both approaches, we created 6 toy data sets, each with its own data-generating process and complexity. Our results on the toy data show that we can achieve decent results on data with none or some random noise. Yet, on the real-world data both approaches performed poorly which suggests that the machine logs at hand are weakly related to the failures and as such are not informative enough for successful failure prediction.20222022-07-22 08:00:00Magistrsko delo/naloga138469sl