Reliability of Multiphysical Systems Set
coordinated by
Abdelkhalak El Hami
Volume 7
First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
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© ISTE Ltd 2017
The rights of Brigitte Chebel-Morello, Jean-Marc Nicod and Christophe Varnier to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2017942387
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-84821-938-0
Due to the evolution of technology, IT, and organizational approaches, industrial equipment is becoming more and more complex and automated. This complexity is a source of various incidents and faults that cause considerable damage to items, the environment and people. Obviously, the reliability of the equipment has an impact on the safety of items and people and, when maintenance is neglected, it can lead to incidents involving prohibitive costs, stemming from interruption of production, replacing items, etc. A lack of maintenance and its impact on the reliability of the equipment can lead to catastrophic consequences for the environment in cases of contamination. This can entail evacuation operations and environmental cleaning without, nevertheless, being able to completely remove the pollution in the area.
In order to prevent risks, companies must use reliable equipment, which should be well maintained by an efficient and well-organized maintenance system. Correct maintenance extends the lifetime of the equipment while contributing to better global performance. For this reason, maintenance has a strategic role in industry, and today it represents an essential task within a production system.
An effective maintenance policy provides technical, economic and social advantages. It is coherent with the idea of sustainable development and makes it possible, on the one hand, to increase the availability of industrial systems and, on the other hand, to lengthen their lifecycle. From the point of view of economics, it reduces the cost of failures and, as a result, increases the profit of the product. The emergence of predictive maintenance, based on fault prognostics and, more generally, on PHM (Prognostics and Health Management) enables:
Classical maintenance strategies such as corrective, preventive and predictive maintenance are composed of business processes such as the upkeep, repair, or monitoring of an equipment’s health state, its monitoring, fault detection, failure diagnostics or fault prognostics. Although these processes can be studied separately, it is wiser to integrate them into a PHM cycle, which can be considered as an adaptation of the OSA-CBM architecture. This cycle is described in part 1 of the book From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics [GOU 16], starting from data acquisition, its processing by means of different modules of the cycle (data processing, detection, diagnostics, prognostics, decision and human machine interface (HMI)) and finishing with the decision and its presentation via suitable HMI interfaces for maintenance operators. This part is dedicated to the first modules of the cycle, from data acquisition to prognostics, proposing different monitoring and prognostic methods.
The purpose of the present book, which follows [GOU 16], is to tackle the other phases of PHM. These include the traceability of data, information and knowledge (first part of the book), and the ability to make decisions accordingly (second part of the book).
Maintenance implementation requires qualifications and contributes to the development of maintenance technicians. Our work, within a context of quality policy advocating for the continuous improvement of practices, is in line with a present challenge for a company, which is to provide the employees with the right information at the right moment, in order to allow them to work in the best conditions, and therefore to improve their skills. To maintain the items in a well-functioning state and to anticipate any failure, the maintenance operators need to be able to access all kinds of support services related to different maintenance strategies.
Companies should progress by transforming their activities through the development of a learning culture, which is the only alternative for maintaining a permanent state of innovation. Learning culture means sharing knowledge and cooperative work among members of a company. As a result, knowledge is considered to be the driving force of productivity and economic growth. An emergence of the knowledge management problem is taking place. Creating, capitalizing and sharing knowledge thus become a challenge that any company faces.
In this book, we address the expert maintenance knowledge of a maintenance company, the formalization and the manipulation of this knowledge. The maintenance operations, combined with technical advancements and new information and communication technology, have entailed an evolution of maintenance systems towards systems that integrate smart modules, which communicate and collaborate among each other. It is in this industrial and scientific context that the works described in the first three chapters are entirely situated. A knowledge management approach has been implemented in order to analyze the maintenance processes used by a maintenance company, with the goal of making an overview of support systems to be developed, and of being able to make them available as maintenance support services for the company’s employees.
Actually, timely access to information concerning a product or a piece of equipment provides a better monitoring of the latter and allows us, for example, in the case of dangerous products, a better handling of this product, thus improving safety.
Information traceability is a vital element for ensuring the monitoring of a product or equipment in time, during its whole lifecycle. Over the last few years, Product Lifecycle Management (PLM) systems have been increasingly used to manage business operations and data generated by events and actions that involve the product1.
Lifecycle refers to a set of phases that can be identified as the different stages of life of a product, from its creation to dismantlement. It is composed of three main phases:
The PLM concept is much more than an issue of visualizing and transforming data. It includes processes (the flow of data among the operators and the flow of resources according to competency) and methods (practices and techniques established along the process by using product data generated during each life stage of the product). This translates into three fundamental elements that constitute the basis of the PLM concept: ICT managing remote information systems, the processes and the methodology, which evolve along the lifecycle phases of a product (Figure I.1).
PLM services based on the Web, contrarily to PDM (Product Data Management) systems, do not limit themselves to facilitating the exchange of information regarding the product among heterogeneous product data management systems [GUN 08], but they can be a platform of collaborative development with the integration of data originated at scattered locations. PLM widens the field of application of PDM systems in order to provide a large company with more information concerning their product.
The availability of information during each phase of a product’s lifecycle enables the sharing of information among the players of different cycle stages and the exploitation of this knowledge to improve the decisions to be made with respect to the product.
Figure I.1. Elements of the PLM concept
During the management phase of the product’s middle of life, or MOL, a lot of data is gathered on the field for monitoring and controlling the product’s life state and for keeping a record. Information issued from the product’s beginning of life, BOL, is necessary for analyzing the product’s structure and for understanding its behavior.
Within the context of safety of items and people, standards and laws are imposed in order to be able to trace the history of each product with the aim of ensuring a reliable, safe and traceable supply, and enabling the recovery of information required to understand post-mortem any anomalous event, whichever its seriousness.
The second part of the book focuses on the concept of decisions based on expert knowledge of the system and on estimations provided by prognostics.
Indeed, sharing information regarding the product and its lifecycle process is vital for ensuring its durability. Knowledge of the product’s history sheds light on this management, and it can provide information regarding the implemented maintenance policy, which has a non-negligible effect on the product’s operating state. Actually, an effective maintenance policy yields technical, economic and social advantages:
Today, technological evolution enables the equipment to communicate and to provide information regarding the different ongoing events related to it. Therefore, it is possible to trace its operation and malfunctioning during its lifecycle. One of the key features of PLM systems is the availability of information, which can be easily accessed by the operators related to the product, and the intelligence integrated in their lifecycle. The idea is to propose a set-up of a so-called intelligent equipment, as in McFarlane’s definition, which facilitates access to embedded and remote information.
In this book, we address the product’s middle of life and, in particular, the implemented maintenance policy, as well as its impact on the other lifecycle phases. In fact, information issued from the MOL phase can be used:
Chapter 1 illustrates a smart tracing system and its architecture connected to a remote maintenance platform. An infrastructure of smart products is proposed, which enables the equipment to be connected and capable of knowledge capitalization based on maintenance ontology.
Chapter 2 proposes a maintenance platform with a particular attention to knowledge, in order to guarantee the traceability of information along its lifecycle, and thus to be able to implement decision support systems.
An application of this intelligent traceability has been implemented on a ski lift and its brief description is given in Chapter 3.
Chapter 4 illustrates a bibliographic overview of different decision-making approaches in the context of PHM. The aspects of scalability of this decision phase (temporal granularity and description degree) are illustrated as well. This chapter is an opportunity to show the importance of decisions within the PHM process.
A first implementation of decisions is the object of Chapter 5. It adds a new strategic dimension to maintenance by means of the anticipation which it enables. Therefore, we speak of predictive maintenance. This chapter illustrates an example of optimization of predictive maintenance starting from information that is issued from the prognostic phase of PHM. This optimization consists of reducing the maintenance related costs via appropriate planning.
Finally, Chapter 6 develops an original approach for involving production resources with respect to demand. A further dimension is added to the planning phase by varying the utilization conditions of each piece of equipment with respect to its health state, with the aim of lengthening the production lifespan of the whole system before maintenance.
The book concludes with a summary analysis and perspectives regarding this emerging domain, since without traceability, knowledge and decision, any prediction of the health state of a system cannot be exploited.