Fault diagnosis has been addressed in many research communities, leading to a variety of fault diagnosis techniques.For a user to decide which fault diagnosis methods are suitable for a specific application scenario is thus a non-trivial task.Benchmarks are used to provide the community with a holistic understanding of the landscape of available and newly developed fault diagnosis methods.After a long hiatus, the DX Competition is revived with three fault diagnosis benchmarks: SLIDe, LUMEN, and LiU-ICE.The purpose of the benchmarks is to inspire fault diagnosis research with challenging industrial problems.The benchmarks share a common code structure and similar performance metrics to simplify the adaptation of diagnosis system solutions to the different case studies.

This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver's corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications.

By running simulation models on FPGAs, their execution speed can be significantly improved, at the cost of increased development effort. This paper describes a project to develop a tool which converts simulation models written in high level languages into fast FPGA hardware. The tool currently converts code written using custom C++ data types into Verilog. A model of a hybrid electric vehicle is used as a case study, and the resulting hardware runs significantly faster than on a general purpose CPU.