Accurate and robust detection of anomalies in time series is fundamental to many established methods of intelligent automation. For example, accurate anomaly detection is a prerequisite for successful predictive maintenance or security monitoring. In the recent past, neural networks built in autoencoder structures have become the state of the art in anomaly detection. A new network architecture in deep learning — the Kolmogorov-Arnold networks — coupled with wavelet-based activation functions (wavKAN) currently promises better performance in time series analysis than classical multilayer perceptrons (MLP). This paper evaluates wavKAN-based autoencoders on the UCR Time Series Anomaly Archive, a repository of 250 datasets including industrially relevant applications. The results of the evaluation show that wavKAN-based autoencoders detect anomalies more accurately than MLP-based autoencoders, while relying on a smaller number of parameters. WavKANs thus offer a novel approach to anomaly detection that reduces hardware requirements in industrial applications, making them particularly suitable for edge devices.

Many small and medium-sized enterprises lack large datasets and AI expertise, limiting their ability to apply traditional AI methods. However, they often possess valuable yet underutilised experimental data. This paper introduces an interpretable AI workflow tailored for such scarce data environments. It guides users through experimental design, data labelling, Decision Trees, and Active Learning to optimise processes efficiently. A bread roll baking use case illustrates the workflow’s practical value and transferability to other industrial settings.

The production of plastic fibers is a complex process, where the quality of the produced fibers is crucial for their performance in applications like fire-resistant concrete. Their quality is determined through visual inspections of dispersion tests, which are chemically treated samples of the produced fibers. However, this process is prone to more conservative ratings by lab personnel, as to avoid missing existing defects. This leads to unnecessary blade replacements and increased costs. To support the lab personnel in their rating process, a machine learning based pipeline is proposed. It consists of a classification model and explainable artificial intelligence (XAI) methods to visualize the presence of agglomerations, which are the sole determinant of the dispersion test’s rating. The goals are to provide lab personnel with a second opinion on the quality of the dispersion tests, whilst having only scarce labeled data available. Additionally, the suitability of different XAI methods and their pre- and post-processing steps is evaluated. The proposed pipeline is able to classify the dispersion tests with an accuracy of 85.92 %. The results highlight Layer-wise Relevance Propagation and HiResCAM as the most suitable XAI methods for this use case.

Time series forecasting allows businesses to gain insights into market trends and plan production, storage, and sales in advance. There is a shift towards applying machine learning (ML) models for forecasting on hierarchical sales datasets. However, large historical training data is not always available, particularly in markets where sales are not frequent but rather occur weekly or monthly. In such cases, current ML approaches often require years of training data. This study focuses on small datasets and evaluates the potential of ensemble methods to improve prediction accuracy. Forecasting models are assessed on three hierarchical sales datasets from two companies. Sixteen traditional statistical and ML models were evaluated using customized test periods for each dataset. The results show that the best-performing model depends on the dataset's characteristics. A seasonal naive model showed effective seasonality detection, while a baseline model robustly outperformed complex models in one of the datasets. Among traditional statistical models, theta and exponential smoothing produced similar results, and a statistical ensemble outperformed in larger prediction horizons. ML models showed mixed results, with ML ensembles showing significant advantages on the largest dataset. The study demonstrates the importance of model choice relative to dataset specifics, with traditional statistical approaches often outperforming their machine-learning counterparts on small datasets.

Detecting redundancies between information sources is essential for applications both in machine learning and information fusion. State-of-the-art redundancy metrics, such as correlation coefficients or mutual information, are based on probabilistic concepts. In technical multi-source systems information is often uncertain but also incomplete. Thus information is often provided with uncertainty distributions (probabilistic or possibilistic). In this paper a redundancy metric is proposed which is embedded in the framework of possibility theory applicable incomplete and uncertain information. The metric considers the consistency between sources, the specificity of pieces of information, and the range of observed information over the frame of discernment. The redundancy metric is designed to be cautious since incorrect identification of redundancies affects both machine learning and information fusion applications negatively. A machine learner may be deprived of information, whereas an information fusion system, relying on false assumptions, may, e.g. , incorrectly assess sources as unreliable. The proposed redundancy metric is qualitatively evaluated on information sources of three technical datasets.

On-site aircraft repairs are gaining in importance due to the susceptibility of carbon fibre reinforced polymers to damage. Repairs themselves are required to be inspected for quality, preferably cost- and time-efficiently. This paper presents an approach for the inspection of repaired composites based on guided Lamb waves. The focus is on cost-effective signal excitation and effective signal processing. Lamb waves are excited with piezoelectric transducers at the resonance frequency of the material under test. Measured signals are processed with a complex wavelet transform to improve damage detection. The proposed approach is evaluated on two test specimens, one of which has a defect in the adhesive bond.

The aim of sensor orchestration is to design and organise multi-sensor systems both to reduce manual design efforts and to facilitate complex sensor systems. A sensor orchestration is required to adapt to non-stationary environments, even if it is applied in streaming data scenarios where labelled data are scarce or not available. Without labels in dynamic environments, it is challenging to determine not only the accuracy of a classifier but also its reliability. This contribution proposes monitoring algorithms intended to support sensor orchestration in classification tasks in non-stationary environments. Proposed measures regard the relevance of features, the separability of classes, and the classifier's reliability. The proposed monitoring algorithms are evaluated regarding their applicability in the scope of a publicly available and synthetically created collection of datasets. It is shown that the approach (i) is able to distinguish relevant from irrelevant features, (ii) measures class separability as class representations drift through feature space, and (iii) marks a classifier as unreliable if errors in the drift-adaptation occur.

Multisensor systems are susceptible to sensor ageing effects as well as to environmental changes. Due to these effects, the distribution of sensor measurements may change over time, which is referred to as sensor drift. A multisensor system which adapts to drift by self-monitoring is more durable, requires less manual maintenance, and provides information of higher quality. This contribution proposes an approach for detecting and adapting to sensor drift. The proposed detection algorithm determines the reliability of a sensor based on fuzzy pattern classifiers and a consistency measure. By this means, the inherent redundancy in multisensor systems is exploited to detect drift. Detected drift leads then to a retraining of the classifier on batched data guided by information fusion. The retraining incorporates the estimated magnitude of the drift. The proposed algorithms are evaluated in comparison with state-of-the-art methods in the scope of a publicly available dataset. It is shown that the drift detection algorithm yields results similar to the benchmark algorithm but is less computationally complex. Relearning with the drift-adapted approach results in more robust classifiers with regard to potential future drift.

The current trend towards mass customisation requires adaptive, modular, and flexible production systems. The installation, configuration, and monitoring of such systems are becoming increasingly time-consuming, expensive, and complex tasks. The related challenges are met by self-organisation and information fusion techniques. Distributed self-organising systems are robust, scalable, and inherently modular, whereas information fusion techniques reduce the complexity of information from distributed sources. The combination of both, an automated design of information fusion systems taking advantage of self-organising methods, is an open and active research field. This contribution proposes an approach for agent-based intelligent sensor nodes which cooperate towards designing an information fusion system relying on semantic self-descriptions. The focus is on communication and making collective decisions including task allocations and election processes. The performance of the proposed approach is evaluated in comparison to a centralised state-of-the-art design system. It is shown that the proposed approach scales similarly, but due to its distributed nature, it is more robust to device failures.