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Editorial - (2014) Volume 3, Issue 2

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Vahid Ebrahimipour*
oratory of Physical Asset Integrity Management, Department of Industrial Engineering and Mathematics, École Polytechnique de Montréal, H3C 3A7, Montréal, Canada
*Corresponding Author: Vahid Ebrahimipour, Laboratory of Physical Asset Integrity Management, Department of Industrial Engineering and Mathematics, École Polytechnique de Montréal, H3C 3A7, Montréal, Canada, Tel: 514-340-4711, Fax: 514 340 4463 Email:


Integrated Intelligent Equipment Health Management System (IIEHMS) is a systematic engineering design Framework that includes multi-functional disciplines (such as mechanical, electrical, optical and software engineering, quality assurance, and manufacturing) with their partners and suppliers works together to achieve a green topology plant with respect to life cycle cost. The proposed structural framework is defined as fitness for operation at any time during plant life cycle. Plant life cycle includes the entire spectrum of activities; beginning with the identification of need and extending through plant design and development, production and/or construction, operational use and sustaining maintenance and support, and plant retirement and material disposal.

Keywords: Ontology engineering, Knowledge modeling and acquisition, Adaptive fault diagnosis, Green production system


Current energy production chains are carbon-based or derived from a carbon source (i.e. oil and gas), which have negative impacts on environment because of CO2 emission and other greenhouse gases. Growing world energy demand from fossil fuels plays a key role in the upward trend in CO2 emissions. Petrochemical and chemicals industries among other industrial sectors have much devastative influences on CO2 emissions through producing organic and inorganic products embodied carbon such as olefins, aromatics ammonia and carbon black or oil and gas combustions. The Organization of the Petroleum Exporting Countries (OPEC) states that the average portion of CO2 emissions from oil & gas usage is expected to double by 2050 [1]. With current available technologies, the options for replacing fossil fuels or switching to less carbon fossil fuels are limited. These fossil fuels will likely remain to be the predominant source of energy in industry at least for this century. However, increasing costs for waste disposal and emissions control, growing international regulatory pressure, and increasing public demands for environmental quality are forcing nations to lay foundations for global agreement to curb CO2 emissions, for example the UN climate negotiation in Qatar, KYOTO protocol and OECD. To mitigate or eliminate adverse environmental impacts due to specific products and processes, national efforts have been conducted in order to switch to more efficient technologies, and to life cycle and system optimization approaches [2]. The Waste and Resources Action Program (WRAP) highlights life cycle and system optimization as two complementary approaches for achieving sustainable production process in USA, UK, EU and Japan [3]. These two approaches attempt to obtain the optimal resource efficiency and sufficiency through waste reduction, lean production, industrial synergies, extended product life time, efficient use of equipment, and equipment life time optimization [4].


The methodology is composed of ontology correlated-layers, and AND-OR partitioning matrices for fault propagation reasoning and failure mode partitioning [5-8]. Ontology correlated-layers matrix considers fault propagation as a sequence of actions to achieve a specific failure mode. In other words, each failure mode can be described as cascade events in which the start and finish points are root causes and equipment errors. The matrix defines cascade events through parsing (reaching to context-free grammars and may be classified as unification grammars) of equipment function and then the mapping from the syntactic form to a logical form by the semantic interpretation (deriving the syntactic structure of a sentence toward the construction of the behavior understanding model), and finally mapping from both syntactic and logical forms (Preconditions, Body, Effects) to a final representation by a contextual knowledge component (using knowledge of the present context to identify the particular consequences of a certain sentence) [5]. The context-free grammars are supported by ISO 14224 [5] and OREDA [6], in which an exhaustive technical terminology and format related to maintenance, safety are explain by relevant experts. To provide syntactic and logical forms, we employ FMEA, FTA, ETA & HAZOP to identify cascade events [4,8-10]. To reach a pure knowledge and interpretation from a causality context (logic form), prepared by semantic interpretation, which can be developed and shared among users, we need a base to define and support a common data model for long-term data integration, access and exchange. In this matter, we applied ISO 15926 as a core concept on which ontology is expanded to exchange and share knowledge among users [66,64 and 70]. Through ISO 15926's concept, logic form in causality, and cause-consequence-event relationship are modeled clearly and then fed on the next step of contextual interpretation process.


The main anticipated and expected result of this methodology is the development of An Integrated Intelligent Equipment Health Management System, and its adaptation to meet energy sustainability, system application integration and HSE requirements based on Qatar National Priorities Research Program. The system integrates an intelligent framework that takes into consideration the equipment's life cycle and the environmental requirements for a conscientious production to achieve low-carbon economy and energy efficiency and sufficiency. As a special part of this overall system is an expert system ‘shell' which contains modules for maintenance management, defect analysis and failure analysis, and which emphasizes the importance of the environment and safety risk reduction. The capabilities of the elaborated system will be tested by a pilot run, with data extracted specific standards and handbooks.


  1. Department of Energy and Climate Change, “Introduction to Climate , Change Agreements,” CCA-A02. DECC: London, 2009.
  2. Brown T, Gambhir A, Florin N, Fennell P “Reducing CO2 emissions from heavy industry: a review of technologies and considerations for policy makers”, Grantham Institute for Climate Change, No 7, February 2012.
  3. Ebrahimipour V,Qurayshi SF, Shabani A,Maleki B (2011) “Reliability Optimization of Multi-State Weighted K-out-of-N Systems by Fuzzy Mathematical Programming and Genetic Algorithm” International Journal of Systems Assurance Engineering and Management,  2:312-318.
  4. Ebrahimipour V,Yacout S “A Novel Ontology-Based Schema to Support Maintenance Knowledge Representation-Case Study: Pneumatic Valve,” IEEE Transaction on System,Man &Cybernetics, under modification.
  5. Ebrahimipour V, Rezaie K, Shokravi S (2010)“An Ontology Approach to Support FMEA Studies,” Expert System With Applications, 37:671-677.
  6. Azadeh A,  Ebrahimipour V, Bavar P (2010) “A Fuzzy Expert System for Pump Failure Diagnosis to Improve Maintenance Policies - Case Study: Centrifugal Pumps in Petrochemical Industry”, Journal of Expert System With Applications, 37:627-639.
  7. Venkatasubramanian V, Zhao J, Viswanathan S (2000) “Intelligent systems for HAZOP analysis of complex process plants”, Computers and Chemical Engineering, 24:2291-2302.
  8. Ebrahimipour V, Suzuki K (2006) “A Synergetic Approach for Assessing and Improving Equipment Performance in Offshore Industry Based on Dependability”, Journal of Reliability Engineering & System Safety 91:10-19.
  9. Tian X,  Ping Cao1 Y, Chen S(2011) “Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor” Int. J. Adapt. Control Signal Process, 25:813-830.
Citation: Vahid Ebrahimipour (2014) Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production. Int J Swarm Intell and Evol Comput 3:e105.

Copyright: © 2014 Ebrahimipour V. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.