دانلود مقاله ISI انگلیسی شماره 21854
عنوان فارسی مقاله

سیاست نگهداری پیشگویانه بر اساس داده های فرآیند

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21854 2010 7 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Predictive maintenance policy based on process data
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Chemometrics and Intelligent Laboratory Systems, Volume 103, Issue 2, 15 October 2010, Pages 137–143

کلمات کلیدی
- نگهداری پیشگویانه - حالت تخریب های متعدد - پیش بینی گسل احتمالی - قابلیت اطمینان - تحلیل مؤلفه های اصلی
پیش نمایش مقاله
پیش نمایش مقاله سیاست نگهداری پیشگویانه بر اساس داده های فرآیند

چکیده انگلیسی

For the ‘under maintained’ and ‘over maintained’ problems of traditional preventive maintenance, a new predictive maintenance policy is developed based on process data in this article to overcome these disadvantages. This predictive maintenance method utilizes results of probabilistic fault prediction, which reveals evolvement of the system's degradation for a gradually deteriorating system caused by incipient fault. Reliability is calculated through the fault probability deduced from the probabilistic fault prediction method, but not through prior failure rate function which is difficult to be obtained. Moreover, the deterioration mode of the system is determined by the change rate of the calculated reliability, and several predictive maintenance rules are introduced. The superiority of the proposed method is illustrated by applying it to the Tennessee Eastman process. Compared with traditional preventive maintenance strategies, the presented predictive maintenance strategy shows its adaptability and effectiveness to the gradually deteriorating system.

مقدمه انگلیسی

The annual cost of maintenance has been reported to go up to 15% for manufacturing companies, 20%–30% for chemical industries [1], 40% for iron and steel industries [2]. Thus developing new maintenance technologies and arranging proper maintenance scheduling has become more and more important to enhance production and economic efficiency. Despite this economic factor, the maintenance of equipment always has a major impact on system reliability, availability and security. The evolvement of maintenance technology has experienced three different types, i.e. corrective maintenance (CM), preventive maintenance (PvM) and predictive maintenance (PdM). CM, the earliest maintenance technology, means repairing a system only after a breakdown or an obvious fault. PvM means performing repair, service, or replacement for a component or system at a fixed period to prevent a breakdown. PdM decides whether or not to do system maintenance based on the condition of the system. CM and PvM are the traditional maintenance policies, which may cause low reliability or high maintenance cost. While PdM utilizes appropriate condition monitoring and maintenance management technologies, which can greatly increase the efficiency and profitability of industrial production [3]. Although PdM is an effective approach to promote system reliability, the implementation of this new PdM is not an easy task for uncertainties and less of fault data in practical processes. Most of the present predictive maintenance approaches are based on a classical assumption, that is, the system failure can be explained by a stochastic deterioration process [4], [5], [6] and [7], which is consistent with many real failure processes, such as erosion [8], wear [9], and so on. They always supposed that the system state at time t can be summarized by a random aging variable Xt [5], which starts from 0, increases like a Gamma stochastic process until a predefined threshold. Then, maintenance rules can be developed according to system deterioration. However, this method suffers from the disadvantage that it is very difficult to find this aging variable Xt in real systems. Here, a new predictive maintenance policy is developed based on process variables, which can be obtained at a very low cost in modern industries. In many former references, reliability, the variable reflecting the system state in the present article, is calculated through a system failure function. However, it is very difficult and almost unreliable to obtain a real failure function for a complex industrial system. In this article, reliability can be deduced from the fault probability achieved by our former proposed probabilistic fault prediction method, which is much easier to realize. At last, the predictive maintenance can be implemented based on these real-time process variables. The remainder of this paper is organized as follows. In Section 2, the idea of two different deterioration modes is introduced. Details of the PdM policy are explained in Section 3. In Section 4, several indexes are introduced to evaluate the maintenance performance. The effectiveness of the integrated method is illustrated by applying it to the Tennessee Eastman process in Section 5. Finally, Section 6 discusses the conclusions and some future research directions.

نتیجه گیری انگلیسی

In this article, a predictive maintenance method based on probabilistic fault prediction has been presented for system subject to gradually deterioration process. The proposed maintenance policy takes into consideration of the deterioration caused by incipient fault which is predicted using the probabilistic fault prediction method developed in our preview work. Such policy has been proved to be more effective in the simulator of the Tennessee Eastman process. It represents a first promising attempt for the maintenance of degradation systems with only process variables information, which is much easier to be implemented. One direction for future research is related to the necessity to extend the analysis to real process. Another direction is to extend this continuous process maintenance to batch processes maintenance, which shows different process data characteristics. In conclusion, the proposed predictive maintenance policy has provided a significant attempt to make maintenance decision according to real-time process variables.

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