Maintenance services logistics for wide geographically dispersed applications, such as oil transfer systems via pipelines or waste water treatment, have high costs and standard approaches usually lead to sub-optimal solutions. These systems are composed by a huge number of devices, often placed in inaccessible areas with a large distance between them. In such applications autonomous Intelligent Maintenance System (IMS) are capable to estimate their health conditions, can be used to forecast maintenance needs and to optimize maintenance schedule, therefore reducing the overall costs. Artificial Immune Systems (AIS) are a set of algorithms inspired by bio-immune systems that have features suitable for applications in IMS. AIS have distributed and parallel processing that could be useful to model large production systems. This chapter proposes an architecture for a Distributed IMS using Artificial Immune Systems concepts to face the challenges described and explore in-site learning. Each equipment has its own embedded AIS, performing a local diagnosis. If a new fault mode is detected, this information is evaluated and classified as a new non-self pattern, and included in the "vaccine". In this way, what is learned by one AIS can be propagated to the others. This proposal is modeled and implemented using multi-agent systems, where every autonomous IMS is mapped to a set of local agents, while the communication and decision process between IMSs are mapped to global agents. The chapter also describes the preliminary results deriving from the application of the proposed approach to a case study.
Abstract-Maintenance is a practice in manufacturing that had never been available to remote control and management until the introduction of web-connected portable smart devices. In the last years several studies and applied research have been conducted for achieving this objective in an efficient way and with the aim to enhance the business activity related to. Remote access and role-specific data distribution can become the next level upgrade of maintenance, diagnostic and flow control management using smart sensors, actuators, and smart consumer devices (smartphone, tablet, etc.).In this project, a real case is presented, an Italian company, the end user of the project, tried to achieve this goal creating with the all consortium, a new web-services based server application in order to have remote access to the data stream, which permits to have the machine status available on the web, very strict time responses, a better user profiling and innovative control system based on smart devices monitoring real time machine data and sending notification sounds when needed. The result is a platform connecting, using the Internet of Things (IoT) paradigm, industrial machineries with a smart device android app and with a web application running on a normal browser.
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