سیستم خبره برای تعمیر و نگهداری بر اساس قابلیت اطمینان در صنایع شیمیایی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22356||2000||13 صفحه PDF||سفارش دهید||5551 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 19, Issue 1, July 2000, Pages 45–57
A new framework for the implementation of reliability centered maintenance (RCM) in the initial design phases of industrial chemical processes was developed and implemented. Fuzzy reasoning algorithms were designed to evaluate and assess the likelihood of equipment failure mode precipitation and aggravation. Furthermore, an approximate reasoning scheme which considers local, product, and adjacent machinery effects was constructed to prioritize the equipment failure modes likely to precipitate in the process. The new RCM approach was implemented through an expert system. The computer system reads the process flowsheet generated by ASPEN Plus and, based on relevant machine operating data, it provides the user with the final process RCM availability structure diagram. This availability diagram consists of a listing of all critical machine failure modes likely to precipitate, prioritized according to their overall negative impact on the process, as well as important information on their corresponding local and system effects, and suggested controls for their detection
During the last decades, the need for identifying cost-effective maintenance programs for production plants and manufacturing facilities has generated a proliferation of global analysis methodologies oriented to the development of competent reliability management policies. Among these analytical methods, Reliability Centered Maintenance (RCM), which was first introduced by the civil aviation industry in the 1960s, is, indeed, not only the most frequently used but also the technique that has proven to be the most effective worldwide. The RCM methodology provides a practical and structured approach for arriving at a satisfactory maintenance strategy for each component of a given system. In choosing a strategy, the methodology takes into account safety requirements, maintenance costs, and costs of lost production (Vatn, Hokstad & Bodsberg, 1996). In essence, RCM can be defined as a technique for organizing maintenance activities to be cost-effective. Its central objective is to determine the actions required to ensure that all physical asset continue to fulfill their intended functions in its current operating environment. During the last years, several different frameworks have been adopted by industrial practitioners in order to accommodate the RCM's principles to their increasing equipment maintenance demands. The development of computer software packages that embed either mathematical optimizing algorithms or managerial rules of thumb or heuristics represent the most recent efforts in the area of reliability management modeling. Expert systems have been defined as consulting systems that simulate the problem-solving ability of human experts through the use of expertise drawn from an information base and specific rules employed to interpret such knowledge. Expert systems are structured in three distinct components. The knowledge base is a set of rules about the problem domain, supplied by the expert or obtained through in-depth research. The working memory carries out the tracking of what has been concluded or learned at any stage of a particular consultation. The inference engine evaluates what is true at any given time in the working memory and the knowledge base, resolving conflicts when necessary (Ignizio, 1991).
نتیجه گیری انگلیسی
This research has been focused on the development of a new innovative framework for the implementation of RCM in the early stages of the process design phase. The resulting computer expert system is a valuable tool in assessing and evaluating all relevant equipment failure modes, and their corresponding effects, before the actual process is set up for production. This would reflect in great savings and enhanced results of later preventive maintenance actions. Moreover, this work also represents a pioneer attempt in automating the implementation of RCM via an intelligent computer system. This study has also incorporated a technique that has proven very successful in other areas of knowledge, fuzzy reasoning, in the evaluation and assessment of equipment failure modes. An alternative to the traditional RCM decision tree for prioritizing failure modes was also devised through the development of the Priority Index, which not only takes into account the relevancy of the failure modes local and product effects, but also their likelihood of occurring as well as associated negative consequences on adjacent machinery.