While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is modelagnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD’s potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.

Anomaly Detection (AD) focuses on identifying unusual patterns in complex datasets and systems. While Machine Learning and Decision Support Systems (DSS) are effective for this, simply detecting anomalies often falls short in real-world scenarios, especially in engineering contexts where diagnostics and maintenance are essential. Users need clear explanations behind anomaly predictions to understand the root causes and trust the model. The unsupervised nature of AD complicates the development of interpretable tools. To address this, we propose the Extended Isolation Forest Feature Importance (ExIFFI), a new approach that explains the predictions of the Extended Isolation Forest (EIF), applicable to all Isolation Forest models that split using hyperplanes. ExIFFI provides both global and local explanations by analyzing feature importance. Additionally, we introduce Enhanced Extended Isolation Forest ( EIF + ), an improved version of EIF, designed to better detect unseen anomalies by modifying the splitting strategy of hyperplanes. We compare various unsupervised AD methods across five synthetic and eleven real-world datasets using the Average Precision metric. EIF + consistently outperforms EIF in all scenarios, demonstrating superior generalization. To validate the interpretability, we propose a new metric — A U C F S (Area Under the Curve of Feature Selection) — which uses feature selection as a performance indicator. ExIFFI proves more effective than other unsupervised interpretation methods, excelling in 8 out of 11 real-world datasets and correctly identifying anomalous features in synthetic datasets. Finally, we provide open-source code to encourage further research and reproducibility. • Introduced ExIFFI, an interpretability tool for standard and Extended Isolation Forests. • Developed EIF + , an advanced model enhancing generalization in anomaly detection. • Demonstrated superior performance of ExIFFI and EIF + on synthetic and real-world data. • Contributed open-source code to support research and reproducibility in machine learning. • Applied a novel feature importance metric for unsupervised anomaly detection interpretability.

In this paper, we present the project “VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing” by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions; (2) maintain operations in case of emergency situations; (3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes.

Predictive Maintenance (PdM) aims to detect forth-coming failures in machinery to reduce costs associated with defective products and equipment inactivity. Remaining Useful Life (RUL) estimation is the most common approach in PdM: in this formalization, forecast or regression models aim at predicting the time/process iterations left before machinery loses its operation ability or a failure happens. In the RUL literature, Deep Learning (DL) algorithms are typically the preferred choice because they achieve high performance and can automatically handle the feature extraction phase. Usually, developed DL architectures are application or equipment specific; thus, there is no clear way to select, design, or implement such architectures. However, the research usually does not justify the choice of one architecture over another that may potentially work for the same problem. In addition, many of the reviewed papers do not investigate the computational complexity of these techniques, which is a critical aspect of real-time applications. In this work, we compare the most widely used deep learning architectures for performing RUL estimation in four datasets: two public datasets known in the PdM research community and two confidential industrial datasets. Moreover, we release a library called CeRULEo, to support the research within this field, speeding up the development of RUL models and providing a complete pre-processing pipeline for dataset handling.

Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. We propose a scenario that holds immense appeal for various real-world applications, where a model adapts to handle a stream of machines with distribution shifts Tests on real packaging data proved the feasibility of Continual Learning for addressing such problems. Our study uncovers the limitations of previous algorithms in the Domain Incremental Learning. Our research presents a novel approach for tackling multi-label tasks in Continual Learning, achieving superior performance compared to existing approaches found in the literature. Our method not only achieves optimal performance but also has logarithmic complexity, significantly reducing computation times.

Predictive Maintenance technologies are particularly appealing for Industrial Equipment producers, as they pave the way to the selling of high added-value services and customized maintenance plans. However, standard Predictive Maintenance approaches assume the availability of sensor measurements, and the costs associated with adding sensors or remotely accessing sensor readings may discourage the development of such technologies. In this context, Alarm Forecasting can be very useful as it represents a low-cost alternative or helpful support to sensor-based Predictive Maintenance. In this work, we propose a new formulation for the Alarm Forecasting problem, framed as a multi-label classification task. We present a novel deep learning-based approach called FORMULA (alarm FORecasting in MUlti-LAbel setting). FORMULA leverages Transformer, a popular Neural Network architecture in the field of Natural Language Processing. To cope with alarm imbalance, we draw inspiration from Segmentation and Object Detection. Thus, FORMULA is trained by minimizing the Weighted Focal Loss, which turns out to be very effective in predicting rare alarms. These alarms, even if they are difficult to predict by nature, often are business-critical. We assess the proposed approach on a representative real-world problem from the packaging industry. In particular, we show that it outperforms not only classic multilabel techniques but also models based on recurrent neural networks. As regards the latter, the proposed approach also exhibits a lower computational burden, both in terms of training time and model size. To foster research in the field and reproducibility, we also publicly share the alarm logs dataset and the code used to perform the experiments.Note to Practitioners—This paper was motivated by the problem of monitoring equipment in the scenario of dairy products packaging, under the mild assumption that logs of the alarm generated by the packaging machines are available. This paper proposes an alarm forecasting algorithm. Its goal is to predict if any alarm will occur in the future, based only on past alarm logs. The limits of the considered future window can be defined arbitrarily, so there is enough time to perform corrective actions. Thus, the proposed approach aims to prevent unexpected downtime that would not only hinder productivity but also imply significant material waste. The proposed approach leverages methodologies from Natural Language Processing and Object Detection to deal with rare alarms that are often very informative in the industrial scenario. Besides, both the code and the real-world industrial datasets used to evaluate the methodology are available publicly. Currently, the proposed approach only uses alarm logs. Especially in the context of Industry 4.0, where many sensory data may be available, this is a limitation. Thus, the described approach might be extended by integrating alarm logs with sensory data. This integration is expected to improve the estimation of equipment health state. The results described in this paper may find application not only in the manufacturing sector but also in different areas such as Cyber Security, where log files keep activity records of each process performed.

In the past recent years, Machine Learning methodologies have been applied in countless application areas. In particular, they play a key role in enabling Industry 4.0. However, one of the main obstacles to the diffusion of Machine Learning-based applications is related to the lack of interpretability of most of these methods. In this work, we propose an approach for defining a `feature importance' in Anomaly Detection problems. Anomaly Detection is an important Machine Learning task that has an enormous applicability in industrial scenarios. Indeed, it is extremely relevant for the purpose of quality monitoring. Moreover, it is often the first step towards the design of a Machine Learning-based smart monitoring solution because Anomaly Detection can be implemented without the need of labelled data. The proposed feature importance evaluation approach is designed for Isolation Forest, one of the most commonly used algorithm for Anomaly Detection. The efficacy of the proposed method is tested on synthetic and real industrial datasets.

For a physical layer message authentication procedure based on the comparison of channel estimates obtained from the received messages, we focus on an outer bound on the type I/II error probability region. Channel estimates are modeled as multivariate Gaussian vectors, and we assume that the attacker has only some side information on the channel estimate, which he does not know directly. We derive the attacking strategy that provides the tightest bound on the error region, given the statistics of the side information. This turns out to be a zero mean, circularly symmetric Gaussian density whose covariance matrices can be obtained by solving a constrained optimization problem. We propose an iterative algorithm for its solution: starting from the closed-form solution of a relaxed problem, we obtain, by projection, an initial feasible solution; then, by an iterative procedure, we look for the fixed-point solution of the problem. Numerical results show that for cases of interest the iterative approach converges, and perturbation analysis shows that the found solution is a local minimum.