Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Krysander, Mattias"'
This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially relevant system. Data has been collected from an internal combustion engin
Externí odkaz:
http://arxiv.org/abs/2408.13269
Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is base
Externí odkaz:
http://arxiv.org/abs/2403.18739
In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise de
Externí odkaz:
http://arxiv.org/abs/2403.18664
Safety, reliability, and durability are targets of all engineering systems, including Li-ion batteries in electric vehicles. This paper focuses on sensor setup exploration for a battery-integrated modular multilevel converter (BI-MMC) that can be par
Externí odkaz:
http://arxiv.org/abs/2312.16520
Multi-mode systems can operate in different modes, leading to large numbers of different dynamics. Consequently, applying traditional structural diagnostics to such systems is often untractable. To address this challenge, we present a multi-mode diag
Externí odkaz:
http://arxiv.org/abs/2312.14030
Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create residuals for fau
Externí odkaz:
http://arxiv.org/abs/2305.04670
Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the com
Externí odkaz:
http://arxiv.org/abs/2302.00629
In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individuals where data is collected, and the individuals where the classificat
Externí odkaz:
http://arxiv.org/abs/2203.16121
Publikováno v:
In IFAC PapersOnLine 2024 58(4):360-365
Publikováno v:
In IFAC PapersOnLine 2024 58(4):270-275