A microservice architecture for predictive analytics in manufacturing

Abstract This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

The SPTLC1 p.S331 mutation bridges sensory neuropathy and motor neuron disease and has implications for treatment

Aims SPTLC1-related disorder is a late onset sensory-autonomic neuropathy associated with perturbed sphingolipid homeostasis which can be improved by supplementation with the serine palmitoyl-CoA transferase (SPT) substrate, l-serine. Recently, a juvenile form of motor neuron disease has been linked to SPTLC1 variants. Variants affecting the p.S331 residue of SPTLC1 cause a distinct phenotype, whose pathogenic basis has not been established. This study aims to define the neuropathological and biochemical consequences of the SPTLC1 p.S331 variant, and test response to l-serine in this specific genotype. Methods We report clinical and neurophysiological characterisation of two unrelated children carrying distinct p.S331 SPTLC1 variants. The neuropathology was investigated by analysis of sural nerve and skin innervation. To clarify the biochemical consequences of the p.S331 variant, we performed sphingolipidomic profiling of serum and skin fibroblasts. We also tested the effect of l-serine supplementation in skin fibroblasts of patients with p.S331 mutations. Results In both patients, we recognised an early onset phenotype with prevalent progressive motor neuron disease. Neuropathology showed severe damage to the sensory and autonomic systems. Sphingolipidomic analysis showed the coexistence of neurotoxic deoxy-sphingolipids with an excess of canonical products of the SPT enzyme. l-serine supplementation in patient fibroblasts reduced production of toxic 1-deoxysphingolipids but further increased the overproduction of sphingolipids. Conclusions Our findings suggest that p.S331 SPTLC1 variants lead to an overlap phenotype combining features of sensory and motor neuropathies, thus proposing a continuum in the spectrum of SPTLC1-related disorders. l-serine supplementation in these patients may be detrimental

Achievement of the planetary defense investigations of the Double Asteroid Redirection Test (DART) mission

NASA's Double Asteroid Redirection Test (DART) mission was the first to demonstrate asteroid deflection, and the mission's Level 1 requirements guided its planetary defense investigations. Here, we summarize DART's achievement of those requirements. On 2022 September 26, the DART spacecraft impacted Dimorphos, the secondary member of the Didymos near-Earth asteroid binary system, demonstrating an autonomously navigated kinetic impact into an asteroid with limited prior knowledge for planetary defense. Months of subsequent Earth-based observations showed that the binary orbital period was changed by –33.24 minutes, with two independent analysis methods each reporting a 1σ uncertainty of 1.4 s. Dynamical models determined that the momentum enhancement factor, β, resulting from DART's kinetic impact test is between 2.4 and 4.9, depending on the mass of Dimorphos, which remains the largest source of uncertainty. Over five dozen telescopes across the globe and in space, along with the Light Italian CubeSat for Imaging of Asteroids, have contributed to DART's investigations. These combined investigations have addressed topics related to the ejecta, dynamics, impact event, and properties of both asteroids in the binary system. A year following DART's successful impact into Dimorphos, the mission has achieved its planetary defense requirements, although work to further understand DART's kinetic impact test and the Didymos system will continue. In particular, ESA's Hera mission is planned to perform extensive measurements in 2027 during its rendezvous with the Didymos–Dimorphos system, building on DART to advance our knowledge and continue the ongoing international collaboration for planetary defense

Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network’s weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0%

Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network’s weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0%

A new unsupervised predictive-model self-assessment approach that SCALEs

Evaluating the degradation of predictive models over time has always been a difficult task, also considering that new unseen data might not fit the training distribution. This is a well-known problem in real-world use cases, where collecting the historical training set for all possible prediction labels may be very hard, too expensive or completely unfeasible. To solve this issue, we present a new unsupervised approach to detect and evaluate the degradation of classification and prediction models, based on a scalable variant of the Silhouette index, named Descriptor Silhouette, specifically designed to advance current Big Data state-of-the-art solutions. The newly proposed strategy has been tested and validated over both synthetic and real-world industrial use cases. To this aim, it has been included in a framework named SCALE and resulted to be efficient and more effective in assessing the degradation of prediction performance than current state-of-the-art best solutions

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0

A cloud-to-edge architecture for predictive analytics

Data management and processing to enable predictive analytics in cyber physical systems, holds the promise of creating insight into the underlying processes, discovering criticalities and predicting imminent problems. Hence, proactive strategies can be adopted, with respect to predictive analytics. This paper discusses the design and prototype implementation of a plug-n-play end-to-end cloud architecture, enabling predictive maintenance of industrial equipment. This is enabled by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm

Innovation Trends in I4.0 enabled by 5G and Beyond Networks

<p>The Industry 4.0 (I4.0) concept revolutionizes how processes like quality, productivity, customization, and safety are managed in manufacturing. It has developed alongside 5G technology, and the two influence each other significantly. 5G supports the communication needs of I4.0, while I4.0 is a major sector for 5G growth. This whitepaper offers a comprehensive view of I4.0 design principles, driven by collaboration between I4.0 leaders and contributors to 5G development. It also assesses the applicability of 5G for short and mid-term I4.0 challenges based on input from stakeholders. The report touches on evolving 5G technologies, like Deterministic Networking and Digital Twining, and explores the future of ecosystem dynamics, regulation, and sustainability. Ultimately, it provides valuable insights into the synergy between I4.0 and 5G, drawing from leading 5G-PPP Phase III projects.</p&gt