Marcos Zuccolotto

Visualization tool for cyber-physical maintenance systems

Intelligent Maintenance Systems (IMS) are data acquisition/analysis systems for predictive maintenance. The IMS provide autonomously (or semi-autonomously) diagnoses, prognostics and health assessment of components. In these systems, factors such as data acquisition and visualization from equipments are very important. In this context, this study has used a Cyber-Physical Systems (or CPS) approach to consider the various aspects present into a maintenance environment. The CPS is a new paradigm that seeks to combine and coordinate physical and computational elements. In this paper, we propose a 2D/3D visualization tool for cyber-physical maintenance environments. This tool works under HTTP protocol and makes possible the real-time visualization and remotely access, allowing users to view, to edit and to access the maintenance data via web browser.

A Distributed Intelligent Maintenance Approach Based on Artificial Immune Systems

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.

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.

Device layer agents for an artificial immune intelligent maintenance system

Artificial Immune Intelligent Maintenance System (AI2MS) is a proposal for a distributed condition-based maintenance system that applies the concepts of Artificial Immune Systems. This work presents the design of a multi agent system to support the development of the Device Layer Agents, the AI2MS first level. Agents modeling and implementation of the agents are presented, with focus on communication strategies. An ontology is also proposed to represent concepts and relations within the system.

Design Space Exploration of Embedded Systems for Intelligent Maintenance

With the development of intelligent maintenance techniques, the embedded systems that will be used with such algorithms will need increasingly to present more flexibility, combined with high processing speed and low power consumption. Within this context, model based programming associated with automatic platform-specific code generation capabilities are of great interest. This work performs a design space exploration of some algorithms commonly used on intelligent maintenance applications, in this case wavelet package energies and logistic regression, by analyzing the performance and required footprint of different implementations for intelligent maintenance algorithms when executed in hardware and software. Starting point for the comparison was the so called Watchdog Agent™ IMS system, which is currently available both in MATLAB™ and LabVIEW™ environments. Using available code generation tools distinct hardware and software versions are deployed and both the performance of the generated systems as well as some energy and memory metrics of the resources used in FPGA implementations are compared For the validation tests, vibration data collected from a test bench composed by an electric mechanical actuator was used and obtained results confirmed a great variability of the generated solutions in terms of the assessed metrics, clearly indicating that best solution may vary depending on the application requirements.

A biomimetic approach to distributed maintenance management based on a multi-agent system.

The evolution of modern production plants and the rising performance requirements lead to the need for more advanced maintenance systems. In such a context, autonomous Intelligent Maintenance Systems (IMSs) are capable of estimating health conditions, and can be used to forecast maintenance needs and optimize maintenance schedules, therefore reducing the overall costs. In this chapter, an overview of the capabilities of IMSs is shown, and in particular a biomimetic maintenance system implemented using multi-agents is provided, in order to better understand the strength and limits of this approach.

Artificial Immune Intelligent Maintenance System

Artificial Immune Intelligent Maintenance System (AI2MS) is a proposal for a distributed condition-based maintenance system that applies the concepts of Artificial Immune Systems. This work presents its architecture, the current development and the results obtained so far.

Analyzing and processing intelligent maintenance systems data to use for planning spare parts supply chains

An Intelligent Maintenance System (IMS) provides data about the status of technical devices. Based on sensorial input, IMS analyzes the data and forecasts failures of these devices. The forecasted failures can be used to improve the spare parts demand forecast results as the gathered condition monitoring information provides a more accurate prognosis about the status of the technical devices. The enhanced demand forecasts in the next step build the foundation for planning the activities like inventory, transport, and maintenance management planning along a spare parts supply chain more adequately. To use the data provided by an IMS for forecasting the spare parts demand, the IMS data has to be analyzed and processed as the current existing IMS does not generate condition monitoring information in the data type, which is needed for enhancing the spare parts demand forecast. For that reason, this paper proposes how the IMS can be adapted for generating the needed data type. This is done by developing an IMS simulator based on the same IMS architecture used in the field. An IMS simulator is developed as in real scenarios restrictions are existing concerning the data collection by the installed IMS in the field.

Open Issues for Interfaces on Spare Parts Supply Chain Systems: A Content Generation Methodology

This paper proposes a methodology for content generation to visualization interfaces applied spare parts supply chain systems. Case studies in the context of transportation planning for oil and gas industry will be investigated in order to validate the methodology and to analyze the results. The goal is to provide a methodology to manage and integrate the information from different systems in order to present effective data in visualization interfaces of supply chain systems.