22 citations · 44 across the 14 of their papers we have counts for
15 papers
SurvCF(t): Counterfactual Explanations for Survival Analysis in Predictive Maintenance Multivariate Time Series Data
Zara Karazian, Panagiotis Papapetrou, Sindri Magnússon +2
Predictive maintenance relies on accurate Remaining Useful Life estimation, often formulated using survival analysis over multivariate time-series data. While modern deep survival…
Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems
Arman Mohammadi, Mattias Krysander, Daniel Jung +1
Deep neural networks has been increasingly applied in fault diagnostics, where it uses historical data to capture systems behavior, bypassing the need for high-fidelity physical mo…
The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study
Daniel Jung, Erik Frisk, Mattias Krysander
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 rele…
Usage-Specific Survival Modeling Based on Operational Data and Neural Networks
Olov Holmer, Mattias Krysander, Erik Frisk
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…
Neural Network-Based Piecewise Survival Models
Olov Holmer, Erik Frisk, Mattias Krysander
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 functio…
Structural Diagnosability Analysis of Switched and Modular Battery Packs
Fatemeh Hashemniya, Arvind Balachandran, Erik Frisk +1
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 ba…
Fault Diagnosability Analysis of Multi-Mode Systems
Fatemeh Hashemniya, Benoït Caillaud, Erik Frisk +2
Multi-mode systems can operate in different modes, leading to large numbers of different dynamics. Consequently, applying traditional structural diagnostics to such systems is ofte…
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
Theodor Westny, Joel Oskarsson, Björn Olofsson +1
Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a…
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
Theodor Westny, Joel Oskarsson, Björn Olofsson +1
Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we int…
Energy-Based Survival Models for Predictive Maintenance
Olov Holmer, Erik Frisk, Mattias Krysander
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…
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data
Erik Jakobsson, Erik Frisk, Mattias Krysander +1
In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individu…
Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
Victor Fors, Björn Olofsson, Erik Frisk
An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC),…
Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques
Theodor Westny, Erik Frisk, Björn Olofsson
The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews whi…
A Realistic Simulation Testbed of A Turbocharged Spark-Ignited Engine System: A Platform for the Evaluation of Fault Diagnosis Algorithms and Strategies
K. Y. Ng, E. Frisk, M. Krysander +1
Research on fault diagnosis on highly nonlinear dynamic systems such as the engine of a vehicle have garnered huge interest in recent years, especially with the automotive industry…
Design and Selection of Additional Residuals to Enhance Fault Isolation of a Turbocharged Spark Ignited Engine System
K. Y. Ng, E. Frisk, M. Krysander
This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from…