Modeling of mechanisms for reconfigurable and distributed manufacturing control system

Transcription

1 6th Doctoral Conference on Computing, Electrical and Industrial Systems DoCEIS'15 Modeling of mechanisms for reconfigurable and distributed manufacturing control system Robson M. da Silva, (Presenter) Edson H. Watanabe, Mauricio F. Blos, Fabricio Junquiera, Diolino J. Santos Filho, Paulo E. Miyagi, Lisbon April-2015

2 Content Introduction Relationship to cloud-based solutions State of the art and related works Modeling of diagnosis mechanism for RDMCS Application example Conclusions and Further Work

3 Introduction Business processes in manufacturing systems can modify Furthermore, distributed subsystems need communicating and collaborating In preview studies, we propose a method to model a Reconfigurable and Distributed Manufacturing Control System (RDMCS) The method combines characteristics of different techniques Petri net (PN) to allow a communication protocol and integration of the design phases Holonic and Multi-Agent System (HMAS) to facilitate the conception and implementation of RDMCS Service-Oriented Architecture (SOA) paradigm to consider interoperability between distributed subsystems This paper extends that method presenting the modeling of reconfiguration mechanisms

4 Relationship to cloud-based solutions This proposal is related to this vision due, e.g. avoiding repetition and overlapping tasks facilitating the analysis and communication between different design staffs allowing reconfiguration flexibility considering social and dynamic environment of the business processes being modular and collaborative system

5 State of the art SOA and HMAS can provide a framework for integrating heterogeneous and distributed subsystems Petri nets can ensure a systematic implementation of design specification Production Flow Schema programs for industrial controllers Input and Output Place Transition

6 Modeling of diagnosis mechanism for RDMCS Modeling holons starts using Production Flow Schema (PFS) The PFS model is more detailed to generate the IOPT models The control architecture is subdivided into product holon task holon supervisor holon operational holon

7 Modeling of diagnosis mechanism for RDMCS Modeling holons starts using Production Flow Schema (PFS) The PFS model is more detailed to generate the IOPT models The control architecture is subdivided into

8 Modular manufacturing system

9 Modular manufacturing system Final Products sensors and actuators

10 Modeling PrHs Final Products a cover [co] a spring [s] a piston (black [bp] or aluminum [ap]) a cylinder body (black [bcb], red [rcb] or aluminum [acb])

11 Modeling THs

12 Modeling THs

13 Dynamic modeling

14 Dynamic modeling

15 Dynamic modeling

16 Dynamic modeling

17 Dynamic modeling

18 Dynamic modeling

19 Dynamic modeling

20 Dynamic modeling

21 Control strategy for D-WS

22 Modeling OpHs [1A] [1B2] [1B1] [1Y1]

23 Modeling OpHs normal states: advanced and retreated [1A] [1B2] [1B1] [1Y1]

24 Modeling OpHs fault state: advanced stuck [1A] [1B2] [1B1] [1Y1]

25 Modeling OpHs [1A] [1B2] [1B1] [1Y1]

26 Composition of OpHs models

27 Composition of OpHs models auxiliary places

28 Composition of OpHs models Guards

29 Composition of OpHs models Output actions

30 Modeling diagnoser (for fault diagnosis). The steps for this i. defining the observable and non-observable events

31 Modeling diagnoser (for fault diagnosis). The steps for this i. defining the observable and non-observable events

32 Modeling diagnoser (for fault diagnosis). The steps for this ii. diagnoser construction, initially considering normal operation, then the sequence of possible events, in according to control strategy

33 Modeling diagnoser (for fault diagnosis). The steps for this ii. diagnoser construction, initially considering normal operation, then the sequence of possible events, in according to control strategy

34 Modeling diagnoser (for fault diagnosis). The steps for this ii. diagnoser construction, initially considering normal operation, then the sequence of possible events, in according to control strategy

35 Modeling diagnoser (for fault diagnosis). The steps for this iii. linking the states obtained according to input or output signal of actuators and sensors

36 Modeling diagnoser (for fault diagnosis). The steps for this iv. linking the diagnoser and sub-diagnoser by means of transitions and guard to the events

37 Modeling diagnoser (for fault diagnosis). The steps for this iv. linking the diagnoser and sub-diagnoser by means of transitions and guard to the events possible fault states

38 Conclusions This proposal allows modeling complex system structures of Reconfigurable and Distributed Manufacturing Control System (RDMCS) avoiding repetition and overlapping tasks through reuse collaboration among distributed subsystems reconfiguration flexibility utilization of web tools for automatic generation of controller languages cloud engineering Further works involve extending the research to other control systems, as well as, the proposal of decision making mechanisms

39 Acknowledgment Supported by agencies CNPq, CAPES and FAPESP Thank you! Robson Marinho da Silva,