A Distributed Intelligent Maintenance System based on Artificial Immune Approach and Multi-Agent Systems

Logistics for maintenance services for a wide geographically dispersed applications, such as oil transfer systems via pipelines or waste water treatment, have high cost and standard approaches usually lead to sub-optimal solutions. In this paper, an innovative architecture able to improve the performance, called Artificial Immune Intelligent Maintenance System (AI2MS) was proposed. This architecture merges the advantages of two different methodology: Artificial Immune System (AIS) and Multi Agent System (MAS) in order to to build up an intelligent maintenance system that can operates either in an autonomous or cooperative way to forecast the maintenance needs (time to fault and required supply parts) and improve the maintenance schedule and costs

Artificial immune intelligent maintenance system: diagnostic agents

The Artificial Immune Intelligent Maintenance System (AI2MS) is an architecture proposal for a Distributed Intelligent Maintenance System (IMS) using Artificial Immune Systems concepts. Equipment has its own embedded AIS, performing a local diagnosis. 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. This paper describes the diagnostic agents implementation of the AI2MS and present some preliminary results deriving from the application of the proposed approach to a case study

An Application of Artificial Immune System in a Wastewater Treatment Plant

Guaranteeing the continuity and the quality of services in network plants is a key issue in the research area of asset management. Especially when the plants are located in a wide area where machines are not continuously monitored by the operators. In particular, the pervasive adoption of smart sensors could be able to develop intelligent maintenance system through an elaboration of data coming from the machines: this data could be processed by diagnostics algorithms to warn preventively the fault status of the components or machines monitored. The algorithms’ structure is contained in a multiple system of agents that have different tasks to manage both the single machine and the information exchanged within the whole system. This paper aims to present an application of Artificial Immune System defining, for each plant section, the kind of agents employed and the related sensors that must be adopted to collect the useful data. In order to provide a practical example, the structure of an Artificial Immune System has been implemented in a wastewater treatment plant where the agents are tested with noteworthy results

A distributed intelligent maintenance approach based on artificial immune systems

Maintenance services logistics for wide geographically dispersed appli-cations, 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 paper proposes an architecture for a Distributed IMS using Artificial Immune Systems concepts to face the challenges de-scribed 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 paper also describes the preliminary results deriving from the application of the proposed approach to a case study

Industrial requirements for the development of a prognostics platform

In today's global competitive marketplace, there is an intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. Moreover, fast growth of communication technologies, computer and information technologies have changed the pattern of maintenance. New maintenance approaches, namely prognostics and e-maintenance, have emerged and are gradually replacing the traditional maintenance solutions. The paper shows the industrial requirements for the design of an e-maintenance and prognostics platform. The research is carried out within the Intellimech Consortium, a newly Italian initiative in the applied research field, which includes 25 industrial companies and aims at contributing and spreading out knowledge and managerial culture on maintenance engineering

A smart web-based maintenance system for a smart manufacturing environment

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