ENBIS-22 Trondheim Conference

Session

CONTRIBUTED Reliability 3 + Mining

28 Jun 2022, 16:30

EL2

Conveners

CONTRIBUTED Reliability 3 + Mining

• Bjarne Bergquist (Luleå University of Technology)

8. Lifetime Drift Model for Discrete Data for Semiconductor Devices

Lukas Sommeregger (Infineon Technologies Austria AG), Dr Horst Lewitschnig (Infineon Technologies Austria AG)

28/06/2022, 16:30

Reliability

Prognostics and health management and calculation of residual useful life are important topics in automotive industry. In the context of autonomous cars, it is imperative to lower the residual risk to an acceptable level.
On the semiconductor level, various advanced statistical models are used to predict degradation on the basis of accelerated life time stress tests. The change of electrical...

83. Comparing statistical and machine learning models for predictive maintenance in solar power plants

Gøran Sildnes Gedde-Dahl (NMBU)

28/06/2022, 16:50

Reliability

In solar power plants, high reliability of critical assets must be ensured---these include inverters, which combine the power from all solar cell modules. While avoiding unexpected failures and downtimes, maintenance schedules aim to take advantage of the full equipment lifetimes. So-called predictive maintenance schedules trigger maintenance actions by modelling the current equipment...

70. Preprocessing and process mining for business-to-business sales forecasting

Osmo Kauppila (University of Oulu)

28/06/2022, 17:10

Mining

When products are made-to-order, sales forecasts must rely on data from the sales process as a basis for estimation. Process mining can be used for constructing the sales process from event logs, but it also provides characteristics from the process itself that can be utilized in prediction algorithms to create the actual sales predictions. Based on literature, the encoding of process...