Predictive maintenance reduces downtime and maintenance costs by early detection of equipment failures using sensor data and machine learning techniques. Despite its potential, the progress of this technique is usually affected by limited access to real-world industrial data. Industrial datasets are often not shared due to confidentiality concerns, competitive interests, and regulatory constraints such as the GDPR. This article discusses the challenges of industrial data access, explores alternative approaches to enable data-driven collaboration, and outlines common technical challenges associated with real-world datasets. Addressing these challenges through collaborative, privacy-preserving data sharing is essential for enhancing maintenance strategies and enabling closer collaboration between industry and academia.

With the rise of low-cost launches, miniaturized space technology, and commercialization, the cost of space missions has dropped, leading to a surge in flexible Earth observation satellites. This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. It aims to maximize observation efficiency while minimizing energy consumption. During population initialization, the algorithm uses chaos mapping and opposition-based learning to enhance diversity and global search, reducing the risk of local optima. It integrates Q-learning into an improved multi-objective grey wolf optimization framework, designing state-action combinations that balance exploration and exploitation. Dynamic parameter adjustments guide position updates, boosting adaptability across different optimization stages. Moreover, the algorithm introduces a reward mechanism based on the crowding distance and inverted generational distance (IGD) to maintain Pareto front diversity and distribution, ensuring a strong multi-objective optimization performance. The experimental results show that the algorithm excels at solving the MOAEOSSP, outperforming competing algorithms across several metrics and demonstrating its effectiveness for complex optimization problems.

This paper investigates the reliability of explanations generated by large language models~(LLMs) when prompted to explain their previous output. We evaluate two kinds of such self-explanations -- extractive and counterfactual -- using three state-of-the-art LLMs (2B to 8B parameters) on two different classification tasks (objective and subjective).

Our findings reveal, that, while these self-explanations can correlate with human judgement, they do not fully and accurately follow the model's decision process, indicating a gap between perceived and actual model reasoning.

We show that this gap can be bridged because prompting LLMs for counterfactual explanations can produce faithful, informative, and easy-to-verify results. These counterfactuals offer a promising alternative to traditional explainability methods (e.g. SHAP, LIME), provided that prompts are tailored to specific tasks and checked for validity.

This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output. We evaluate two kinds of such self-explanations (SE)—extractive and counterfactual—using state-of-the-art LLMs (1B to 70B parameters) on three different classification tasks (both objective and subjective). In line with Agarwal et al. (Faithfulness versus plausibility: On the (Un)reliability of explanations from large language models. 2024. https://doi.org/10.48550/arXiv.2402.04614), our findings indicate a gap between perceived and actual model reasoning: while SE largely correlate with human judgment (i.e. are plausible), they do not fully and accurately follow the model’s decision process (i.e. are not faithful). Additionally, we show that counterfactual SE are not even necessarily valid in the sense of actually changing the LLM’s prediction. Our results suggest that extractive SE provide the LLM’s “guess” at an explanation based on training data. Conversely, counterfactual SE can help understand the LLM’s reasoning: We show that the issue of validity can be resolved by sampling counterfactual candidates at high temperature—followed by a validity check—and introducing a formula to estimate the number of tries needed to generate valid explanations. This simple method produces plausible and valid explanations that offer a 16 times faster alternative to SHAP on average in our experiments.

Predicting failures and maintenance time in predictive maintenance is challenging due to the scarcity of comprehensive real-world datasets, and among those available, few are of time series format. This paper introduces a real-world, multivariate time series dataset collected exclusively from a single anonymized engine component (Component X) across a fleet of SCANIA trucks. The dataset includes operational data, repair records, and specifications related to Component X while maintaining confidentiality through anonymization. It is well-suited for a range of machine learning applications, including classification, regression, survival analysis, and anomaly detection, particularly in predictive maintenance scenarios. The dataset’s large population size, diverse features (in the form of histograms and numerical counters), and temporal information make it a unique resource in the field. The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from an internationally well-known company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.

In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.

CounterFair is a group counterfactual search algorithm that detects and minimizes biases among sensitive groups and identifies relevant subgroups inside these sensitive groups based on shared counterfactual instances. We investigate the latter capability, analyzing the found subgroups from the perspective of fairness based on counterfactual reasoning, in order to evaluate whether they present different biases with respect to each other and to the sensitive feature groups they belong to. We perform these measurements on the subgroups extracted by CounterFair over six binary classification datasets, providing figures and their respective analysis on the presence of bias.

Uncontrolled diabetes can lead to severe complications and Intensive Care Unit (ICU) admissions. This study presents an explainable machine learning model using electronic health records to predict ICU admissions and estimate hospital stay duration for diabetic patients. AdaBoost model outperformed other models on ICU admission prediction, while CatBoost exhibited superior performance in estimating ICU length of stays among diabetic patients admitted to the emergency departments. The results demonstrate the potential of explainable machine learning in ICU risk assessment and can aid healthcare providers in early intervention and resource utilization. The clinician and the proposed model agree on the top 25 features identified by Shapley Additive exPlanations (SHAP) methods for predicting ICU admission, but they differ in the ranking of the top five most significant predictors.

Agile satellite imaging scheduling plays a vital role in improving emergency response, urban planning, national defense, and resource management. With the rise in the number of in-orbit satellites and observation windows, the need for diverse agile Earth observation satellite (AEOS) scheduling has surged. However, current research seldom addresses multiple optimization objectives, which are crucial in many engineering practices. This article tackles a multiobjective AEOS scheduling problem (MOAEOSSP) that aims to optimize total observation task profit, satellite energy consumption, and load balancing. To address this intricate problem, we propose a strategy-fused multiobjective dung beetle optimization (SFMODBO) algorithm. This novel algorithm harnesses the position update characteristics of various dung beetle populations and integrates multiple high-adaptability strategies. Consequently, it strikes a better balance between global search capability and local exploitation accuracy, making it more effective at exploring the solution space and avoiding local optima. The SFMODBO algorithm enhances global search capabilities through diverse strategies, ensuring thorough coverage of the search space. Simultaneously, it significantly improves local optimization precision by fine-tuning solutions in promising regions. This dual approach enables more robust and efficient problem-solving. Simulation experiments confirm the effectiveness and efficiency of the SFMODBO algorithm. Results indicate that it significantly outperforms competitors across multiple metrics, achieving superior scheduling schemes. In addition to these enhanced metrics, the proposed algorithm also exhibits advantages in computation time and resource utilization. This not only demonstrates the algorithm’s robustness but also underscores its efficiency and speed in solving the MOAEOSSP.

Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, most AI-based prognostics and health management in the automotive industry ignore textual descriptions of the experienced problems or symptoms. With this study, however, we propose an ML-assisted workflow for automotive fault nowcasting that improves on current industry standards. We show that a multilingual pre-trained Transformer model can effectively classify the textual symptom claims from a large company with vehicle fleets, despite the task’s challenging nature due to the 38 languages and 1357 classes involved. Overall, we report an accuracy of more than 80% for high-frequency classes and above 60% for classes with reasonable minimum support, bringing novel evidence that automotive troubleshooting management can benefit from multilingual symptom text classification.