An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases – from their design to their deployment, to their management –, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks

Design methodologies for space systems in a System of Systems (SoS) architecture

L'abstract è presente nell'allegato / the abstract is in the attachmen

Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs

The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems. In recent times, machine learning (ML) methodologies have emerged as potent instruments for assessing design quality-of-results at the Register-Transfer Level (RTL) or Boolean level, aiming to expedite design exploration of advanced RTL configurations. In this presentation, we introduce an innovative open-source framework that translates RTL designs into graph representation foundations, which can be seamlessly integrated with the PyTorch Geometric graph learning platform. Furthermore, the Verilog-to-PyG (V2PYG) framework is compatible with the open-source Electronic Design Automation (EDA) toolchain OpenROAD, facilitating the collection of labeled datasets in an utterly open-source manner. Additionally, we will present novel RTL data augmentation methods (incorporated in our framework) that enable functional equivalent design augmentation for the construction of an extensive graph-based RTL design database. Lastly, we will showcase several using cases of V2PYG with detailed scripting examples. V2PYG can be found at \url{https://yu-maryland.github.io/Verilog-to-PyG/}.Comment: 8 pages, International Conference on Computer-Aided Design (ICCAD'23

Automatic Fracture Orientation Extraction from SfM Point Clouds

Geology seeks to understand the history of the Earth and its surface processes through charac- terisation of surface formations and rock units. Chief among the geologists’ tools are rock unit orientation measurements, such as Strike, Dip and Dip Direction. These allow an understanding of both surface and sub-structure on both the local and macro scale. Although the way these techniques can be used to characterise geology are well understood, the need to collect these measurements by hand adds time and expense to the work of the geologist, precludes spontaneity in field work, and coverage is limited to where the geologist can physically reach. In robotics and computer vision, multi-view geometry techniques such as Structure from Motion (SfM) allows reconstructions of objects and scenes using multiple camera views. SfM-based techniques provide advantages over Lidar-type techniques, in areas such as cost and flexibility of use in more varied environmental conditions, while sacrificing extreme levels of fidelity. Regardless of this, camera based techniques such as SfM, have developed to the point where accuracy is possible in the decimetre range. Here is presented a system to automate the measurement of Strike, Dip and Dip Direction using multi-view geometry from video. Rather than deriving measurements using a method applied to the images, such as the Hough Transform, this method takes measurements directly from the software generated point cloud. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines

An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment wear before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry

Modeling of motion primitive architectures using domain-specific languages

Nordmann A. Modeling of motion primitive architectures using domain-specific languages. Bielefeld: Universität Bielefeld; 2016

Common Data Fusion Framework : An open-source Common Data Fusion Framework for space robotics

Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Framework and explain how it is used. Sensor data are registered and fused within the Common Data Fusion Framework to produce comprehensive 3D environment representations and pose estimations. The proposed software components to model this process in a reusable manner are presented through a complete overview of the framework, then the provided data fusion algorithms are listed, and through the case of 3D reconstruction from 2D images, the Common Data Fusion Framework approach is exemplified. The Common Data Fusion Framework has been deployed and tested in various scenarios that include robots performing operations of planetary rover exploration and tracking of orbiting satellites

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

Detecting Feature-Interaction Hotspots in Automotive Software using Relational Algebra

Modern software projects are programmed by multiple teams, consist of millions of lines of code, and are split into separate components that, during runtime, may not be contained in the same process. Due to these complexities, software defects are a common reality; defects cost the global economy over a trillion dollars each year. One area where developing safe software is crucial is the automotive domain. As the typical modern vehicle consists of over 100 million lines of code and is responsible for controlling vehicle motion through advanced driver-assistance systems (ADAS), there is a potential for these systems to malfunction in catastrophic ways. Due to this risk, automotive software needs to be inspected to verify that it is safe. The problem is that it can be difficult to carry out this detection in code; manual analysis does not scale well, search tools like grep have no contextual awareness of code, and although code reviews can be effective, they cannot target the entire codebase properly. Furthermore, automotive systems are comprised of numerous, communicating features that can possibly interact in unexpected or undefined ways. This thesis addresses this problem through the development of a static-analysis methodology that detects custom interaction patterns coined as hotspots. We identify several classes of automotive hotspots that describe patterns in automotive software that have the possibility of manifesting as a feature interaction. To detect these hotspots, this methodology employs a static, relational analysis toolchain that create a queryable model from source code and enable engineer defined queries to be run on the model that aim to reveal potential hotspots in the underlying source code. The purpose of this methodology is not to detect bugs with surety but work towards an analysis methodology that can scale to automotive software systems. We test this hotspot detection methodology through a case study conducted on the Autonomoose autonomous driving platform. In it, we generate a model of the entire Autonomoose codebase and run relational algebra queries on the model. Each script in the case study detects a type of hotspot we identify in this thesis. The results of each query are presented