Internet of Things - Enabled visual analytics for linked maintenance and product lifecycle management

When closed loop product lifecycle management was first introduced, much effort focused on establishing ways to communicate data between different lifecycle phase activities. The concept of a smart product, able to communicate its own identity and status, had a key role to play to this end. Such a concept has further matured, benefiting from internet things-enabled product lifecycle management advancements. Product data exchanges can now be brought closer to the point of end use consumption, enabling users to become more proactive actors within the product lifecycle management process. This paper presents a conceptual approach and a pilot implementation of how this can be achieved by superimposing middle of life relevant product information to beginning of life product views, such as a 3D product CAD model. In this way, linked maintenance data and knowledge become visual features of a product design representation, facilitating a user’s understanding of middle-of life concepts, such as occurrence of failure modes. The proposed approach can be particularly useful when dealing with product data streams as a natural visual analytics add-in to closed loop product lifecycle management

Recurrent Neural Networks for real-time distributed collaborative prognostics

We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained

AgentChat: Multi-Agent Collaborative Logistics for Carbon Reduction

Heavy Good Vehicles (HGVs) are the second largest source of greenhouse gas emissions in transportation, after cars and taxis. However, HGVs are inefficiently utilised, with more than one-third of their weight capacity not being used during travel. We, thus, in this paper address collaborative logistics, an effective pathway to enhance HGVs' utilisation and reduce carbon emissions. We investigate a multi-agent system approach to facilitate collaborative logistics, particularly carrier collaboration. We propose a simple yet effective multi-agent collaborative logistics (MACL) framework, representing key stakeholders as intelligent agents. Furthermore, we utilise the MACL framework in conjunction with a proposed system architecture to create an integrated collaborative logistics testbed. This testbed, consisting of a physical system and its digital replica, is a tailored cyber-physical system or digital twin for collaborative logistics. Through a demonstration, we show the utility of the testbed for studying collaborative logistics.Comment: This paper includes 12 pages, 14 figures, and has been submitted to IEEE for possible publicatio

Towards the deployment of customer orientation: A case study in third-party logistics

Customer orientation concerns the degree to which an organisation focuses on customers, recognises their desires and places meeting their needs as a first priority. As managing the needs of individual customers in supply chains become increasingly important, logistics companies have been recognising customer orientation as a critical aspect of their success. This study explores some of the challenges in the deployment of customer oriented logistics systems and argues that the so-called product intelligence model can provide an approach for developing such systems. Using an industrial case study, in this paper we examine customer orientation for a third-party logistics provider by examining both the development of information systems that enable the offering of exible logistics offerings to the end customer and the impact of providing these offerings on a company's performance. We conclude with a set of functionalities required by information systems of logistics providers that wish to enhance customer orientation in their offering

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Current trends on ICT technologies for enterprise information s²ystems

The proposed paper discusses the current trends on ICT technologies for Enterprise Information Systems. The paper starts by defining four big challenges of the next generation of information systems: (1) Data Value Chain Management; (2) Context Awareness; (3) Interaction and Visualization; and (4) Human Learning. The major contributions towards the next generation of information systems are elaborated based on the work and experience of the authors and their teams. This includes: (1) Ontology based solutions for semantic interoperability; (2) Context aware infrastructures; (3) Product Avatar based interactions; and (4) Human learning. Finally the current state of research is discussed highlighting the impact of these solutions on the economic and social landscape

Recommender systems and market approaches for industrial data management

Industrial companies are dealing with an increasing data overload problem in all aspects of their business: vast amounts of data are generated in and outside each company. Determining which data is relevant and how to get it to the right users is becoming increasingly difficult. There are a large number of datasets to be considered, and an even higher number of combinations of datasets that each user could be using. Current techniques to address this data overload problem necessitate detailed analysis. These techniques have limited scalability due to their manual effort and their complexity, which makes them unpractical for a large number of datasets. Search, the alternative used by many users, is limited by the user’s knowledge about the available data and does not consider the relevance or costs of providing these datasets. Recommender systems and so-called market approaches have previously been used to solve this type of resource allocation problem, as shown for example in allocation of equipment for production processes in manufacturing or for spare part supplier selection. They can therefore also be seen as a potential application for the problem of data overload. This thesis introduces the so-called RecorDa approach: an architecture using market approaches and recommender systems on their own or by combining them into one system. Its purpose is to identify which data is more relevant for a user’s decision and improve allocation of relevant data to users. Using a combination of case studies and experiments, this thesis develops and tests the approach. It further compares RecorDa to search and other mechanisms. The results indicate that RecorDa can provide significant benefit to users with easier and more flexible access to relevant datasets compared to other techniques, such as search in these databases. It is able to provide a fast increase in precision and recall of relevant datasets while still keeping high novelty and coverage of a large variety of datasets