Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

A proposal of an architecture for the coordination level of intelligent machines

The issue of obtaining a practical, structured, and detailed description of an architecture for the Coordination Level of Center for Intelligent Robotic Systems for Sapce Exploration (CIRSSE) Testbed Intelligent Controller is addressed. Previous theoretical and implementation works were the departure point for the discussion. The document is organized as follows: after this introductory section, section 2 summarizes the overall view of the Intelligent Machine (IM) as a control system, proposing a performance measure on which to base its design. Section 3 addresses with some detail implementation issues. An hierarchic petri-net with feedback-based learning capabilities is proposed. Finally, section 4 is an attempt to address the feedback problem. Feedback is used for two functions: error recovery and reinforcement learning of the correct translations for the petri-net transitions

Power system fault analysis based on intelligent techniques and intelligent electronic device data

This dissertation has focused on automated power system fault analysis. New contributions to fault section estimation, protection system performance evaluation and power system/protection system interactive simulation have been achieved. Intelligent techniques including expert systems, fuzzy logic and Petri-nets, as well as data from remote terminal units (RTUs) of supervisory control and data acquisition (SCADA) systems, and digital protective relays have been explored and utilized to fufill the objectives. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Data from RTUs of SCADA systems and digital protective relays are used as inputs. Experiential tests have shown that the proposed approach is able to perform accurate fault section estimation under complex scenarios. The evaluation of protection system performance involves issues of data acquisition, prediction of expected operations, identification of unexpected operations and diagnosis of the reasons for unexpected operations. An automated protection system performance evaluation application has been developed to accomplish all the tasks. The application automatically retrieves relay files, processes relay file data, and performs rule-based analysis. Forward chaining reasoning is used for prediction of expected protection operation while backward chaining reasoning is used for diagnosis of unexpected protection operations. Lab tests have shown that the developed application has successfully performed relay performance analysis. The challenge of power system/protection system interactive simulation lies in modeling of sophisticated protection systems and interfacing the protection system model and power system network model seamlessly. An approach which utilizes the "compiled foreign model" mechanism of ATP MODELS language is proposed to model multifunctional digital protective relays in C++ language and seamlessly interface them to the power system network model. The developed simulation environment has been successfully used for the studies of fault section estimation and protection system performance evaluation

Integration of prognostics at a system level: a Petri net approach

This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiach´ıo et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework resides in its efficiency to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about the system state like the probability distribution of end-of-life, information from sensors, and information coming from expert knowledge. In addition, the proposed methodology allows us to rigorously model the flow of information through logic operations, thus making it useful for nonlinear control, Bayesian updating, and decision making. A degradation process of an engineering sub-system is analyzed as an example of application using condition-based monitoring from sensors, predicted states from prognostics algorithms, along with information coming from expert knowledge. The numerical results reveal how the information from sensors and prognostics algorithms can be processed, transferred, stored, and integrated with discrete-event maintenance activities for nonlinear control operations at system level

Petri Networks in the Planning of Discrete Manufacturing Processes

This chapter puts forward characteristics of selected issues of manufacturing processes planning using the Petri networks technique. It includes references to the extensive literature concerning the use of Petri networks in computer aided planning of discrete production processes. Diversity of these problems is high as it refers both to the methods of modeling and simulation of the course of manufacturing processes, the issue of optimizing these processes and production systems, representation of knowledge on production parts of equipment and integration of planning and production activities in general. The work puts forward example use of a temporary, priority Petri network for modeling and optimizing production systems and manufacturing operations as well as an example of fuzzy interference using the Petri network mechanism

Fuzzy-Petri-Net Reasoning Supervisory Controller and Estimating States of Markov Chain Models

Markov chain models are efficient tools for representing stochastic discrete event processes with wide applications in decision and control. A novel approach to fuzzy-Petri-net reasoning generated solution to initial or another state in Markov-chain models is proposed. Reasoning is performed by a fuzzy-Petri-net supervisory controller employing a fuzzy-rule production system design and a fuzzy-Petri-net reasoning algorithm, which has been developed and implemented in C++. The reasoning algorithm implements calculation of the degrees of fulfilment for all the rules and their appropriate assignment to places of Petri net representation structure. The reasoning process involves firing active transitions and calculating degrees of fulfilment for the output places, which represent propositions in the knowledge base, and determining of fuzzy-distributions for output variables as well as their defuzzified values. Finally, these values are transferred to assign the state of Markov-chain decision model in terms of transition probabilities