Flexible Configuration Mechanism of Control and Data Management
In a Process Monitor System
1
LI Wei,
2XU Qiang,
3ZHANG Li
1,College of Information Science & Engineering Chongqing Jiaotong University,
lwcqcn@sina.com.cn
2,
College of Information Science & Engineering Chongqing Jiaotong University,
xuqiang11821@163.com
3,
College of Science Chongqing Jiaotong University, zl_lw@163.com
Abstract
Process automation has occupied an important position in the development of modern industry as encouraged by the national policy on energy conservation. A configured process monitor system is constructed for resolving real problems in modern production, based on a flexible configuration mechanism, and its control logic, group control configuration methods, and data knowledge patterns are designed and discussed, moreover a data knowledge decision flow is also designed for process monitor equipment by adopting data knowledge management logic, finally, the high performance and efficiency of this method have been tested and verified with improved and better services.
Keywords
: Configuration, Data Knowledge, Process Monitor1. Introduction
In recent years, the gradual complexity of large machinery and electric equipment in modern enterprise has resulted in an increasing collection of equipment and production processes. Process control has gradually broken through from bottom to top with more automated equipments. A close coordination within the production, not only among various devices but also among different industrial device components, results in increased capacity and the durability of production equipment. Coordinated and timely monitoring is required to adjust parameter disturbances, promote stable and continuous production, and optimize production to achieve the most efficient and economic operation in the process. Huge losses caused by downtimes are unacceptable.
To better meet production demands, a configured process monitor system, mainly constructed by bus units, general configuration software, may use advanced equipment, modern management, and information technology. The configured and unified control, planning, and management of a production enterprise as a unit for managing optimal operation management and control will then be achieved. In addition, to reduce losses to a minimum and to provide timely and flexible management services for production equipments, manufacturers are required to use configured technical measures, build a configured service system based on special network technology and computer technology, and provide timely technical services. These requirements should lower time and costs.
Configured monitoring is applied as a specific work pattern that considers actual production equipment characteristics and requirements. Configured monitoring utilizes an industrial control network and the Internet to connect mechanical and electrical equipment together. The equipments may be located in different geographical sites and may serve different functions. Configured management, collaborative work, and resource sharing help achieve decentralized control to provide users access to a variety of equipment state parameters, published real-time information, and historical information.
2. Nature-inspired Flexible Pattern
The biological immune system is a productive natural system [1] that enables living beings to survive even when infected by new organic matter or external member. The process of virus recognition by the immune system is similar to the flexible process monitor. Figure.1 is a simple depiction of immune response. In this figure, Ag, Ab, Mφ, B, Bm, Th, Ts, and Tc represent antigen, antibody, phagocytosis cell, B cell, memory B cell, auxiliary T cell, suppressing T cell, and toxic T cell, respectively. Immune cell interactions allow the immune system to have a very strong compatibility
with the environment: a new kind of antigen evolves and produces new B cells; antigens increase, and Ab, B, and B m cells also increase; otherwise, they are reduced. Immune cells already possess general characteristics of agents in essence, and an immune system forms one distributional and autonomous multi-agent system.
Figure.1 Process of immune response
In ensuring the enhanced continuity, high speed, systematic flow, and automation of an industrial production and an enterprise network management, a process control system can be constructed using intelligent agents with a number of interactions for completing monitor tasks. In the monitor system, which has a few agent control groups (ACU), mutual cooperation can be obtained among different types, and some exclusion exists among similar types. This feature is very similar to the promotion and inhibition relationship among special immune antibodies. If a control task object is viewed as an antigen, according to the fitness evaluation program and through evolutions of existing ACU agents, new ACUs may be created to improve adaptabilities to complex tasks in a process control system.
Each agent in the ACU works as a highly autonomous, real-time modular unit that is inspired by the immune response mechanism of transferring information. Each ACU can be constructed using agents of detection, control, management, memory, and evolution, the internal structure of which is shown in Figure.2. A detection agent is an integrated state agent identification system that is responsible for drawing present antigen characteristics from their own information to form characteristic modes about antigens. A management agent is an administrator in the ACU that coordinates the behavior of other agents in the same ACU and obtains an overall appraisal of the present state of the ACU. A control agent is responsible for combining all monitoring information, undertaking process control decisions, and sending diagnosis sub-solutions to a management agent for an overall appraisal. An evolution agent is responsible for optimizing and adjusting what an agent possesses, such as domain knowledge, its models, and so on, to obtain suitable antibodies for present antigens when an ACU cannot solve an admitted task. A memory agent is responsible for establishing an antibody-mode storehouse and upgrading this to strengthen the ability for the second response when an unknown antigen mode happens to achieve rapid control.
Ab: Monitor Subtask
Detection
Agent Control Agent
Management Agent Evolution Agent Memory Agent Communiton Agent ACU Other ACUs
3. Key Flexible Configuration Pattern Design
3.1. Immune group control methods
In the current ACU group, some ACUs are randomly selected to form a candidate collection based on their affinity and proportion. When the new process model appears, special immune evolutionary operators are applied on this candidate collection, which includes a cloning operator, a mutation operator, an inhibition operator, and so on. Finally, after calculating the density and then evaluating the affinity of each ACU individual that is different from its mother ACU, N-ACUs are selected to form a new group for complex monitoring tasks. A functional structure of an evolution agent is shown in Figure.3
Data Parameters Models
Task Management & Plan Cooperation Control Information (Data Inquiry) Obtained Results Initial Abs Affinity Computation Clone Selection Cell Clone Affinity Mutation Clone Restrain Immune Selection Environment Detect & Interaction Knowledge Learning Other Agents Knowledge Database Evolution Machine Evolution Agent Figure.3 Evolution agent structure The immune group control method can be analyzed as follows:
i The ACU groups are initialized, and each memory agent establishes its own ACU pattern library
of TCM={tcm1, tcm2, ..., tcmn}.
ii T=k, this monitor system copes with all monitored information, analyzes the current monitor task
sets that are divided into different subtasks, and then assigns various sub-tasks to appropriate ACUs.
iii A management agent determines whether the current ACU is asked for a new evolution. If
necessary, an evolution agent undergoes required optimization and reconstruction for monitoring the information and models inside each agent from this ACU to seek the best antibody mode for the current antigen.
iv A detection agent receives antigen information (sub-tasks) and then extracts special antigen
characteristic collections of PCX (t)={pcx1 (t), pcx2 (t), ..., pcxm (t) | t = k}. If
pcxi (t)
TCM, then a solution exists (antibodies), and step vi applies.v A control agent concentrates all monitored information and monitoring models, completes these
sub-tasks, and makes final sub-conclusions for this ACU.
vi This system synthesizes all sub-conclusions from different ACUs and then evaluates the
integrated conclusions to determine whether the requirements are met. Otherwise, some requirements are sent to special ACUs, and step iii applies.
vii This system delivers completion information to all management agents in different ACUs. A
memory agent may update the antibody pattern library in its own ACU. viii K=k+1; the system moves to the next moment, then proceeds to ii.
3.2. Configuration of flexible agent construction
An ACU is designed based on hierarchical structure. COM components are classified into different function groups according to certain rules. An ACU can be formed using these hierarchical organization of COM components, and some interaction modes can be determined to provide communication through a data support layer, a common object layer, a business layer, and the public and different components. Different layers in an ACU reflect different abstractions of this system, except the data supporting layer. Each layer is built on COM components. Components offer services to others through their interfaces, and different layers can separate changeable parts from stable parts, which can effectively support an ACU evolutionary behavior.
In a monitor system configuration, required integrated applications can be built as a function module in the agent. To meet a monitor configuration requirement, components in every agent should be classified using four different level types: monitor parameter, monitor state, monitor symptom, and monitor operation [2], Figure.4 shows the relationships and includes roles and mutual effects in the monitor.
Figure.4 Component group configuration relation
The component type of monitor parameter is used to represent different fundamental parameters of basic data in the monitor object, such as temperature, amplitude, and so on. The monitor component type state is used to represent different parameter groups of the monitored object. The component type of monitor symptom is a basic unit in monitor operations, which is used to describe a condition or conclusion in monitor operation. The component type of monitor operation includes operation name, premise, conclusion, confidence, and so on. The component type of monitor parameter, monitor symptom, and monitor operation are not independent.
Upon arrival of new monitor symptoms, some corresponding ACUs can evolve to gain a new ACU for achieving monitor state effectively. A monitor system analyzes and resolves the evolution need from the individuals in this system. A new and adaptable individual monitor can be constructed to effectively adapt to dynamic changes in monitored objects, thereby constituting or cutting some fundamental component objects in existing monitor individuals. By adopting some component objects, some adjustments on the different components in the agents enable ACUs to reconfigure themselves. The applicable main configuration mechanism is shown in Table 1.
3.3. Flexible data knowledge configuration
Data knowledge refers to the accumulated cognition and experience from long-term social practices in the objective world. Data knowledge may be described in the form of production knowledge and be
logically organized into four different forms: monitored parameter knowledge class, monitored state knowledge class, monitored symptom knowledge class, and monitored operation knowledge class.
The monitored parameter class is used to store basic parameters that directly reflect how a system or equipment is operating. The monitored state class is an instruction, a judge, or an expression that contains concrete parameter information and describes a specific state entity that can be drawn and created with an actual valued parameter. The monitored symptom knowledge class is a basic unit in monitor operation knowledge, which is a condition of a monitored knowledge class or its conclusion. Monitored knowledge class is used to determine the existence of a given state or a conclusion in premises or conclusions of some diagnosis knowledge, which includes operation name, premise, conclusion, confidence, and so on.
Table 1. Main configuration mechanism
Num Component Agent
1 Replace component NULL
2 Replace components and change their interface
Revise Agent structure 3 Add new operation component Enlarge Agent operation 4 Delete some operation component Delete Agent operations 5 Update operation component Update Agent operation 6 Add new monitor component Add new Agent 7 Delete a monitor component Delete an Agent 8 Update monitor component Update Agent application
The data knowledge can be constructed as a mode of web-based browser/server (B/S) and then combined with a client layer, middle layer, and service database layer. An SQL2005 database software is used to create special data maintenance tables [3, 4], which can be accessed and displayed to provide users with a clear and an intuitive understanding of the current production. All relevant relation patterns are available by analyzing the data flow in the monitor process, and logical relations among different relation tables may be obtained using the SSMS, which is listed in Figure.5.
Monitor operation table
Operation num Operation mark Operation name Operation note Operation premise 1 Premise 1 note Operation premise 2 Premise 2 note Conclusion mark Conclusion description
Monitor symptom table
Symptom num Symptom mark Symptom name Symptom note Symptom factor 1 Sy Factor 1 note Symptom factor 2 Factor 2 note Symptom factor 3 Factor 3 note
Monitor state table
State num State mark State name State note State type Sy State parameter State source
Monitor parameter table
Parameter num Parameter mark Parameter name Parameter note Parameter source Sy
Figure.5 Data knowledge configuration relation
For accuracy, a concept of credibility [5,6] may be used to indicate and address the uncertainty of evidence and maintenance decision-making. A numerical decision in the form of a reliable knowledge transmission can be achieved using credibility calculations of activated data knowledge. Figure.6 shows the decision-making mechanism.
4. Configuration of Monitor Decision Application
Due to the representation in the production form of maintenance knowledge, each maintenance knowledge may be contracted as follows: first, a piece of maintenance knowledge has up to two premises; second, a combinative relationship between premises is only allowed using the “AND” logic for representation; and lastly, only one conclusion exists in knowledge diagnosis. After these, the “decision tree” concept may be introduced to establish a related maintenance decision-making network [7-9].
Figure.6 Data Knowledge decision flow
Table 2. ‘Press action alert’ knowledge
Num Premise 1 Combine Premise 2 Conclusion
1 Low Pressure of S1 Main Motor Failure of SJ1
2 High Pressure of S2 AND Thermal Relay Action of S3 Main Motor Failure of SJ1
3 Skip Retreat of S
3 AND
Limit Error Signal
of S4 Moving Beam Failure of SJ2
4 Security Bar of S5 Moving Beam Failure of SJ2
5 SJ1 Malfunction of SYJ
6 SJ2 Malfunction of SYJ
In data knowledge decision-making, the solving process of a maintenance problem can be mostly shown in the form of an “AND/OR tree.” Using the knowledge expression form and extending the intermediate variable nodes of S23 and S45, a maintenance decision-making network of “press action alert” can be established, as shown in Figure.7.
The data knowledge decision tree shown in Fig.9 is wide and not deep, thus, it is suitable for using a method named by a width-first hunt, which indicates the hunt of others layer by layer from the bottom to the top of this tree after comparing all nodes in the bottom layer [10-12].
If there are some data knowledge as following:
K1: IF the round grind current is too high (0.5) AND it lasts more than the threshold (0.5), THEN the round grind current is in an exception (0.8, 0.6).
K2: IF the analog signals are in the range of 20 mA to 40 mA (0.4) AND the A/D conversion error is accurate (0.6), THEN the normal analog channel is normal (0.9, 0.7).
K3: IF the round grind current is in an exception (0.7) AND the normal analog channel is normal (0.3), THEN the materials in the wheel grinding machine exceed the load limit (0.9, 0.5).
In the monitoring state of “the materials in the wheel grinding machine exceed the load limit,” known initial conditions should be keyed into the two software interfaces of “the faulty equipment information input” and “the performance of the on-site fault information,” which are “the round grind current is too high” with an initial credibility of 0.80, and “it lasts more than the threshold” with an initial confidence of 0.6, “the analog signals is in the range of 20 mA to 40 mA” with an initial credibility of 0.9, “the A/D conversion error is accurate” with an initial credibility of 0.8, respectively.
Figure.7 Data knowledge decision network
Through the “data knowledge decision” module, the reliability of an operation state is derived to be 0.742 by using previous data knowledge in the decision-making mechanism [13], which indicates that "the materials in the wheel grinding machine exceeds the load limit,” occurs with a 0.742 reliability.
5. Conclusions
Labor costs have been steadily rising in recent years, for configurable monitor and manage the requirements of production equipments satisfactorily, a configured process monitor system is constructed by utilizing an industrial control network and the Internet, and a detailed study has presented on the configured monitor and data knowledge management measures, which includes：
(1) The control logic and configured group control methods are discussed, and some adjustments mechanism on the different components in the agents enables ACUs to reconfigure themselves are analyzed.
(2) A data knowledge decision flow is designed for process monitor equipment based on configurable data knowledge management patterns.
The study aims to provide an effective method for meeting the demands of modern production, The configured process monitor system described here may be helpful for improving the automation, intelligence, and production efficiency of production equipments, production investment costs will be reduced, and the healthy development of enterprises will be promoted.
6. Acknowledgements
This work is supported by Natural Science Foundation Project of CQ CSTC(cstc2012jjA40019). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
7. References
[1] P.J.Costa Branco, J. A. Dente, R. Vilela Mendes, "Using Immunology Principles for Fault Detection", IEEE Transaction on Industrial Electronics, vol.50, no.2, pp.362-373, 2003.
[2] Mingdong Ren, Weidong Zhu, Shanglin Yang, "Component Based Architecture of Information System", Mini-Micro System, vol. 25, no.23, pp.1160-1163, 2004.
[3] Wei Ma, Wenzhe Lv, Rui Cao, "Information System Development Tutorial - Using C # and SQL Server 2005", Beijing: Tsinghua University Press, China, 2007.
[4] Jie Yong, Shunlin Song, "Remote Maintenance of Web Project Based on B/S ", Computer Engineering and Design, vol.29, no.21, pp.5502-5506, 2008.
[5] Yinchun Yang, Zhu Jin, "The Application of Ternary Logic in Uncertain Knowledge Representation and Reasoning ", Microelectronics and Computer, vol.21, no. 2, pp.13-17, 2000. [6] Zhiqiang Xie, Xu Yu , Jing Yang, "Research and Improvement of Multi-relational Decision Tree
Learning Algorithm ", Computer Engineering, vol.35, no.8, pp.50-53, 2009.
[7] Qingsong Xing, Yu Yang,, Aijun Liu, Hao Yao, "Research on Knowledge Sharing for Customer Collaborative Product Innovation Based on Knowledge Grid", Journal of Convergence Information Technology, vol.7, no.1, pp.102-112, 2012.
[8] Qinghua Zhang, Guoyin Wang, Xianquan Liu, "Rule Acquisition Algorithm Based on Maximal Granule", Pattern Recognition and Artificial Intelligence,vol.25, no.3, pp.388-396, 2012.
[9] Jianfang Zhou, Chunyan Tang, Zhiyong Xu etc., " Event Tree, Fault Tree, Decision-making Tree and Bayesian Network ", Journal of Hohai University ( Natural Sciences ) ,vol.37, no.3, pp.351-354, 2004.
[10]Min Wang, Niaoqing Hu, Guojun Qin, "Method of Rule Extraction for Fault Diagnosis of a Helicopter Power Train Based on Incomplete Information", Journal of Vibration and Shock, vol.30, no.12, pp.185-190, 2011.
[11]Xiao Xu, Jingmei Zhai, Haitao Liu, "A Knowledge-based Rough Set Model for Manufacturing Decision", Journal of South China University of Technology ( Natural Science Edition ), vol.39, no.8, pp.36-40, 2011.
[12]Hanyan Huang, "An Approach to the Performance Evaluation of Enterprise Knowledge Management with Fuzzy Linguistic Information", Journal of Convergence Information Technology, vol. 7, no. 5, pp. 106 -112, 2012.
[13]Yueyang Zhang, Yu Su, Weihong Guan, "ASP.NET Program Development Sample Collection (C#) ", Beijing: Posts & Telecom Press, China, 2009.
