دانلود مقاله ISI انگلیسی شماره 21823
عنوان فارسی مقاله

نظارت و نگهداری پیشگویانه: مدل سازی و تجزیه و تحلیل از زمان تاخیر خطا

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21823 2006 12 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Monitoring and predictive maintenance: Modeling and analyse of fault latency
منبع

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

Journal : Computers in Industry, Volume 57, Issue 6, August 2006, Pages 504–515

کلمات کلیدی
سیستم های رویداد گسسته - تشخیص - زمان تاخیر خطا - تعمیر و نگهداری پیشگویانه - اتوماتیک به پایان رسیده -
پیش نمایش مقاله
پیش نمایش مقاله نظارت و نگهداری پیشگویانه: مدل سازی و تجزیه و تحلیل از زمان تاخیر خطا

چکیده انگلیسی

This paper presents an effective way of modeling complex systems through identified functioning modes. In the proposed approach, the integration of monitoring in the manufacturing system is facilitated by the development of a generic model. The aim is to propose a monitoring system able of absorbing internal degradation of any variables and ensuring the continuity of the service. The outline of the optimization of the fault latency method is based on two steps is proposed. The first step is the evaluation of fault latency and the second one is the performance evaluation of monitoring process. Timed automata are the modeling tool used for these two steps. The proposed method can be applied to various kinds of processes and gives good results. Indeed, the simulation results, including a serial manufacturing line, substantiate the feasibility of the proposed method and provide a promising potential to spin-off applications in industrial manufacturing system.

مقدمه انگلیسی

In manufacturing systems, wear-out and eventual failures are unavoidable. However, to reduce the rate of their occurrences and to improve the lifetime of equipments, maintenance can be performed with an adequate monitoring. In fact, monitoring in production systems may alert the maintenance team when a given degradation increases above a specified threshold [1], [2], [3] and [20]. This allows a solution to be found before the occurrence of the failure. Modern industry deals with efficient monitoring to improve reliability of equipment and reduce high maintenance cost [5] and [16]. Now, the mission allowed to the monitoring system is not only the detection task but also the identification of fault. To detect and identify any faults occurring in the dynamical system, it is necessary to find the kind location, and a time of fault occurrence [1] and [5]. In discrete–event systems area, the most common monitoring and diagnostic approach are based on dynamic model, which represents only the good functioning. The inputs and outputs of the system under supervision are used to detect the fault [4], [9] and [15]. Model-based diagnostic algorithms use an explicit model of dynamical system under investigation. This model incorporates the knowledge about the faultless and the faulty system behaviour in systematic way for the analysis of the fault symptoms [7] and [8]. For large manufacturing systems, monitoring integration requires specific developments due to the complexity of the models involved [3], [4] and [5]. Furthermore, these developments consume large computing time resources. Recently, process monitoring and diagnostic methods have been developed for discrete–event systems by using timed Petri nets, stochastic automata, timed automata, template languages or Semi-Markov processes [10], [12], [14] and [18]. The main idea of these methods is to simulate nominal or faulty system behaviour with the discrete model. These methods need the structural and functional models of the system. The faulty behaviour is modelled by using a predefined list of eventual faults which can affect the system. The proposed monitoring function for predictive maintenance is a part of global system supervision. Thanks to the available data about the functioning mode of the system, the aim is to detect, localize and diagnose all the wear-out that can affect performance dependability of the system. The monitoring function increases the availability of the system. Indeed, the early fault detection and localization minimizes unavailability of equipment [13] and [19]. The aim is to propose a monitoring system able of absorbing internal degradation of any variables and ensuring the continuity of the service. This paper proposes a new approach based on the control of tasks duration. This method is based on temporal measure, like the end of the task execution. Three functioning modes are defined. The system is in the normal mode for faultless functioning, the degraded mode for functioning, where the temporal measures are in the acceptable margins and the failure mode when the defined tolerance margin is exceeded. The proposed approach reduces the complexity of the model. It is applied to particular equipments in manufacturing system like conveyer, paint station, packaging station… where the time to achieve a task is an significant parameter. For other kind of degradation like the effect of vibration or temperature, it is necessary to use other complementary tools [5], [6], [16] and [17]. This paper presents an effective way of modeling complex systems through identified functioning modes. In the proposed approach, the integration of monitoring in the manufacturing system is facilitated by the development of a defined generic mode. The outline of the optimization of the fault latency method is also developed in the paper. For the manufacturing systems, the aim is to evaluate at first how many time the systems can stay in the degraded state (fault latency). Then, for this optimal time the second goal is to evaluate the performance of the proposed monitoring model in terms of fault coverage. The general principle of the method is described with the used tool: the timed automata. In this paper, we focus on these four steps: • identification of generic modes, • modeling by timed automata, • evaluation of fault latency, • fault coverage and performance evaluation of monitoring process. This method is applied to a class of manufacturing systems: the serial line.

نتیجه گیری انگلیسی

In this paper, a global method for monitoring a manufacturing system is presented. The proposed solution is based on the timed automata as the modelling tool. This approach gives a best way for monitoring dynamic systems by following the tasks duration and detect as earlier as possible eventual faults. The first step of this approach is to evaluate an important parameter for the monitoring: the fault latency. Indeed, the choice of fault latency is critical for the performance of the monitoring system: a too small value will increase the number of false alarms, and a too high value may lead to useless alarm. The principle of this approach shows that the proposed method can be applied to various kinds of processes and give good results. In addition, the approach presented here represents a powerful and convenient tool for implementing monitoring in manufacturing systems. Some of the main advantages in using the timed automata (statechart) are the following: • The tool consists of a practical solution to carry out the state-space combinatorial explosion problem. • Obtaining generic models requires neither an extensive effort nor a deep knowledge of tool used (timed automata). • Using a black box representation allows the representation of complex manufacturing systems by using generic models for machines and buffers. • The timed automata of the entire system are modular, automatically generated, and very useful for monitoring dynamic systems. The advantage of monitoring by time analysis is that the detailed list of failure modes is not required anymore. Thus, a detailed analysis of the system is not needed; which leads to a simpler implementation. Finally, the modelling tool (timed automata), the development of generic model) and the performance analysis method by fault coverage developed in the paper are chosen to make easy implementation by using information and communication technology in industry. Therefore, the current research effort is directed toward performance and integration of predictive maintenance.

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