Abstract Time series data is one of the most popular data modalities in critical domains such as industry and medicine. The demand for algorithms that not only exhibit high accuracy but also offer interpretability is crucial in such fields, as decisions made there bear significant consequences. In this paper, we present ProtoTSNet, a novel approach to interpretable classification of multivariate time series data, through substantial enhancements to the ProtoPNet architecture. Our method is tailored to overcome the unique challenges of time series analysis, including capturing dynamic patterns and handling varying feature significance. Central to our innovation is a modified convolutional encoder utilizing group convolutions, pre-trainable as part of an autoencoder and designed to preserve and quantify feature importance. We evaluated our model on 30 multivariate time series datasets from the UEA archive, comparing our approach with existing explainable methods as well as non-explainable baselines. Through comprehensive evaluation and ablation studies, we demonstrate that our approach achieves the best performance among ante-hoc explainable methods while maintaining competitive performance with non-explainable and post-hoc explainable approaches, providing interpretable results accessible to domain experts.

Contemporary machine learning techniques are capable of extracting complex structure from data in a way that complements or exceeds manual examination, yet, as is welldocumented, many of these techniques suffer from a lack of interpretability. This paper extends previous work on explainable and interpretable machine learning, in particular on the ‘Knowledge-Augmented Clusters (KnAC)’ approach, allowing human users to benefit from uninterpretable ‘black box’ models to extract structure from datasets by clustering and to make this better understandable. One of the key functions of KnAC is to relate expert-annotated clusters to clusters that have been identified by a machine learning method, and then provide a comprehensible explanation, thus clarifying the relationships that KnAC discovered. Our novel contribution in this paper is to examine the usefulness of subgroup discovery as a way to generate comprehensible explanations within KnAC, and to compare this to the existing approach based on the XAI algorithm Anchors through a detailed evaluation. We find that the approach using subgroup discovery performs equally or better in our extensive experimentation testing this on six different datasets.

The development of predictive maintenance (PdM) solutions is one of the key challenges in the industry today. Manufacturing processes are usually well described by the law of physics and mathematical equations, but the irregularity and randomness of the asset degradation process make it a demanding task to model it. This makes physics-driven models insufficient for this kind of problem. On the other hand, data-driven models, mainly Artificial Intelligence (AI), are gaining much interest in research and applications due to their flexibility and robustness. A compromise between these two approaches are hybrid models that take into account the physics of the process and use modern AI methods to learn its behavior. The next challenge for AI models is to provide information on their reasoning to build understading and trustworthiness, which can be achieved through post-hoc Explainable AI (XAI) methods. In this paper, we use a Physics-Informed Autoencoder (PIAE) in a semi-supervised manner to learn the degradation process of work rolls in the cold- rolling process. We incorporate physics knowledge into the AI model by extending its input space and applying feature masking during the prediction phase. The results of the research show that such an architecture is capable of distinguising between low- and high-wear observations. Furthermore, we include the XAI layer in the model, which gives explanations for the prediction of the model through counterfactuals.

Anomaly detection is emerging trend in manufacturing processes and may be considered as part of the Industry 4.0 revolution. It can serve both as diagnostic tool in predictive maintenance task, as well as trace back mechanism for assessing quality of production or services. In this paper we describe and approach for explainable anomaly detection in industrial data which contains sequential and static features. We based our solution on modified autoencoder architecture with Long Short-Term Memory layers. To address a problem of explinability in deep learning and find origin of the anomalies we have engaged the SHAP method, which gives both local and global explanations of the model. Analysis of SHAP explanations allowed us to determine the source of majority of anomalies detected by deep learning model. We demonstrated the feasibility of our approach on synthetic, reproducible dataset and on real-life data gathered from hot rolling industrial process.

Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into decision-making process of AI systems. Most of the work in this area is focused on supervised machine learning tasks such as classification and regression. Unsupervised algorithms such as clustering can also be explained with existing approaches. This is most often achieved by explaining a classifier trained on cluster data with cluster labels as a dependant variable. However, with such a transformation the information about cluster shape and distribution is lost, which may lead to wrong interpretation of explanations. In this paper, we introduce a method that aids end experts in cluster analysis with human-readable rule-based explanations. We use state-of-the-art explanation mechanism on the multidimensional bounding boxes that represent arbitrarily-shaped clusters. We demonstrate our approach on reproducible synthetic datasets.