Automation of Detection and Fault Management Response of Common Last-Mile Loss-Of-Connectivity Outages Within the Access Network

The article to assesses whether it may be possible to recommend a solution to enable automation of the process of detection and fault management of common conclusive loss-of-connectivity last-mile outages, within the access network. To ascertain the utility of the research, UK based MPLS VPN managed service providers, their fault management staff and their business customers, were surveyed using online questionnaires for their views. UK public Internet users were additionally surveyed via five UK Internet forums. UK communication providers offering MPLS VPN solutions were characterised. Access network connectivity technologies and fault management functions were compared, contrasted and analysed. An aspiration for the solution to be beneficial to the largest potential population, meant that current non-proprietary Internet Standard technologies were selected, justified and identified which could be recommended for use. It was found that of the participating survey respondents, two-thirds were in favour of automation. Many current communication provider processes were found to be mostly automated. The article concludes with recommendations of how an automated solution could potentially be enabled. This involves further use of business-to-business interfacing between communication providers, automation of their Fault Management Systems and introducing Bi-Directional forwarding for detection between last-mile active network elements.

Fault Management Architecture Based on a Digital Twin Approach

Fault management of systems is a key component in mission/operation success of each system or technology. Fault management can be implemented into various different applications, power generation, industrial processing, aviation and transportation, and electrical grids with combinations of renewable energy sources. As the complexity of the overall system design increases, reliance on just pure physics-based or pure data-based modeling is shown to be deficient in the accuracy of fault management. This work shows the potential of a combination of digital twin and a fault management algorithm. The algorithm is designed to be robust, accurate, reliable, and fast; it is based on both, physics and data-based model modeling. The algorithm compares physical and data-based approaches to provide the most reliable fault management solution, through a digital twin. The fault management algorithm is designed to use physics-based model validated on real/synthetic data (data-based model).

Digital Twin-Driven Process and Equipment FMECA Generation for Smart Manufacturing Applications

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive digital twin-driven and model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the deployed equipment. In this paper, we present QSI’s approach towards adapting and enhancing their existing model-based systems engineering (MBSE) approach towards a comprehensive digital twin that incorporates constructs necessary for development of a Process Failure Modes and Criticality Analysis (P-FMECA) and integrates that with an Equipment FMECA. The paper will discuss the various levels of automation towards incorporation of these model constructs and their reuse towards automation of the development of the different digital twins and subsequently the automatic generation of the combined Process and Equipment FMECA. This automated ability to develop the integrated FMECA that incorporates both Process-level Failure Modes and Equipment-level Failure Modes allows the system designer and operators to correlate and identify process failures down to their root causes at the equipment-level and thereby producing a comprehensive actionable systems-level view of the entire Smart Manufacturing facility from a fault management design and operations perspective. The paper will present the application of this novel technology for the Advanced Metal Finishing Facility (AMFF) at the Warner-Robins Air Logistics Complex (WR-ALC) in Robins Air Force Base, Georgia, as part of WR-ALC’s initiative towards model-based enterprise (MBE) and smart manufacturing.

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.