This paper presents a benchmark problem for fault diagnosis of an internal combustion engine that has been formulated and solved. The objective is to design a diagnosis system using and incomplete model information training data that only contains a limited set of fault realizations. Six different solutions to the benchmark, that were presented at the IFAC Safeprocess symposium 2024, are described and evaluated. The contribution of this paper is the benchmark and the presentation of six different solutions in one paper. The paper is intended to provide a starting point for engineers and researchers who work with fault diagnosis and monitoring of technical systems.

The deployment of High Capacity Transport (HCT) vehicles is in process in different countries. Although their performance has been assessed through simulations and test-track experiments, a question that remains unanswered is: how do these vehicles perform in real traffic? In this paper, the question is addressed for one of the transient manoeuvres, i.e., a lane change using Naturalistic Driving Data (NDD). First, an algorithm is proposed to extract lane changes from the NDD of HCT vehicles using GPS, road data and IMU signals. Following this, the performance of two A-double combinations is assessed in the extracted lane changes using measures commonly used in performance-based standards (PBS) schemes like offtracking and rearward amplification. The dependency of these measures on the factors such as the vehicle’s speed, load and lateral displacement is investigated. The assessment concludes that the vehicles satisfy the PBS requirements proposed for them and are driven safely in the extracted lane changes.

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 diagnostics algorithm that relies on a multi-mode extension of the Dulmage-Mendelsohn decomposition. We introduce two methodologies for modeling faults, either as signals or as Boolean variables, and apply them to a modular switched battery system in order to demonstrate their effectiveness and discuss their respective advantages.

This article proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.

The focus of this paper is to use Naturalistic Driving Data to understand how the drivers manoeuvre an A-double combination in the roundabouts and evaluate performance in the roundabouts using measures like Low-Speed Swept Path (LSSP) and Tail Swing (TS). The analyses of the steering patterns and speed variations depict that the standard deviations of the responses of the drivers for a given travel direction in a roundabout are within 35o (17 % of the baseline) for the steering wheel angle and 8 km/h (40 % of the baseline) for the speed. It is also found that the cognitive workload of the drivers due to the steering pattern is higher in right turns compared to straight crossings through the roundabout. The performance analyses show a dependency of LSSP on the instantaneous radius obtained from the vehicle's path, and the vehicle's travel direction in the roundabout. LSSP ranges from 7.7 m for a left turn in a roundabout with an inner radius of 12 m to 3.1 m for a straight crossing in a roundabout with a 30 m inner radius. TS is observed in only one roundabout and its magnitude goes up to 0.4 m in a roundabout of 30 m inner radius.

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.

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 part of a solution to sustainable electrification of vehicles. BI-MMC contains switches to convert DC to AC to drive an electric machine. The various configurations of switches result in different operation modes, which in turn, pose great challenges for diagnostics. The study explores diverse sensor arrangements and system configurations for detecting and isolating faults in modular battery packs. Configurations involving a minimum of two modules integrated into the pack are essential to successfully isolate all faults. The findings indicate that the default sensor setup is insufficient for achieving complete fault isolability. Additionally, the investigation also demonstrates that current sensors in the submodules do not contribute significantly to fault isolability. Further, the results on switch positions show that the system configuration has a significant impact on fault isolability. A combination of appropriate sensor data and system configuration is important in achieving optimal diagnosability, which is a paramount objective in ensuring system safety.

A methodology for the generation of representative driving cycles is proposed and evaluated. The proposed method combines traffic simulation and driving behavior modeling to generate mission-based driving cycles. Extensions to the existing behavioral model in a traffic simulation tool are suggested and parameterized for different driver categories to capture the effects of road geometry and variances between drivers. The evaluation results illustrate that the developed extensions significantly improve the match between driving data and the driving cycles generated by traffic simulation. Using model extensions parameterized for different driver categories, instead of only one average driver, provides the possibility to represent different driving behaviors and further improve the realism of the resulting driving cycles.

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users’ future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.

This work describes the collection and properties of the publicly available rock drill fault classification data set rockdrill11, used for the 2022 PHM Conference Data Challenge. The data is collected from a carefully instrumented hydraulic rock drill, operating in normal operation in a test cell while inducing a number of faults. Hydraulic pressure is measured at 50kHz at three different locations, resulting in detailed pressure signatures for each fault. Due to wave propagation phenomena, the system is sensitive to individual differences between different rock drills, drills rigs and configurations. Such differences named "individuals" are introduced in the data by altering certain parameters in the test setup. An important part of the data is therefore the availability of No-fault reference cycles, which are supplied for all individuals. These reference cycles give information on how individuals differ from each other, and can be used to improve classification.