A Multi-agent based Facility Maintenance Planning and Monitoring System: A Case Study

Transcription

1 A Multi-agent based Facility Maintenance Planning and Monitoring System: A Case Study JaeHoon Lee 1, MyungSoo Lee 2, SangHoon Lee 2, SeGhok Oh 2, and JoongSoon Jang 3 1 Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Salt Lake City, Utah, USA, jaehoon.lee@utah.edu 2 XnSolutions, World Merdian 2 Cha, Gasan-dong, Geumcheon-gu, Seoul, Republic of Korea, {myoungsu, protolee, ohsegok}@xnsolution.co.kr 3 Department of Industrial Engineering, Ajou University, San 5, Woncheon-Dong, YeongTong-Gu, Suwon, Republic of Korea, jsjang@ajou.ac.kr Abstract. In this study, we propose a multi-agent based maintenance management system for maintenance planning and facility monitoring. This system is to support decision-making of maintenance engineers by providing up-to-date facility status and reliability assessment. To do so, the system includes; 1) a computerized maintenance management system to store failure and maintenance history, 2) a multi-agent system to collect data from both the database and the facilities in an automated manner, and 3) a decision support function to provide reliability assessment such as expected remaining life times, failure rates, and failure patterns to engineers. A case study of implementing the proposed system in an automotive part production industry are described. Keywords: multi-agent system, maintenance management, computerized maintenance management system, decision support 1 Introduction Decision-making in facility maintenance management is an important part of manufacturing systems in that the facilities are usually expensive resources, and so a small malfunction of them can cause huge loss to the companies. Although information technology (IT) have brought significant benefits to manufacturing systems, facility maintenance planning and operation are still known as an area of relying on know-how of experienced engineers. However, the concept of e- maintenance has been recognized as a powerful way of computerized maintenance management during decades [1]. Use of IT supports decision-making in maintenance management including planning activities, selecting policies, recording history, and predicting facility reliability and maintainability [2][3]. 1 Corresponding author; jaehoon.lee@utah.edu 110

2 There are two noticeable tools that enable computerized maintenance management in a systematic way. One is a computerized maintenance management system (CMMS) which is a data management tool and reliability centered maintenance (RCM) as a reliability assessment and prediction tool. A CMMS is to store maintenance data electronically such as facility profile, failure category, failure history, repair cost, and work schedule. A recent CMMS was enabled by the Web to support ubiquitous access as well as to avoid the expense and distraction of software maintenance, security, and hardware upgrade [4]. RCM is a statistical approach to establish an effective maintenance policy and a routine maintenance program toward a system or a facility. It enables maintenance engineers to focus on preserving core functions of a system or equipment with cost-effective tasks [5]. RCM and CMMSs can be complimentary in that collecting failure data is a fundamental part of RCM and a CMMS can satisfy it. For the reasons, integrating a CMMS into RCM has been implemented [6][7][8]. Nonetheless there are two difficulties which managers and engineers usually have. One is that the data which are required for RCM analysis are physically distributed in manufacturing fields, nonelectronic formatted, and required of data pre-processing. Although a CMMS supports a systematic way of collecting and storing the data, data processing still relies on human analysts. The other is that RCM requires statistical background to the engineers. For the reasons, the quality of RCM implementation has been highly dependent on the experience and skills of RCM analysts [9]. In this paper, we developed an integrated maintenance planning and facility monitoring system using multi-agent technology. Our approach is to integrate the two systems; 1) a multi-agent system (MAS) was used to automate data gathering and processing, 2) a CMMS was also used to provide maintenance data for the MAS. Based on the integration, a decision support function for reliability assessment was added. It provides up-to-date facility status using control charts as well as key indicators from reliability assessment such as expected remaining life or parts, priorities of maintenance tasks, and failure patterns. Maintenance engineers can prevent potential problems of the facilities based on the information. The rest of this paper is as follows. Section 2 describes the design concept, system architecture, and the core functions. In Section 3, we implemented the proposed system for maintenance management of injection molding machines in an automotive part industry in Korea. Conclusion and discussion are also added to represent the contribution and limitation of this study. 2 System development An effective RCM analysis should be based on data collection prepared at right time of decision-making. CMMSs can satisfy these requirements by storing maintenance related data such as operation logs, spare part inventory, test report, failure history, and maintenance protocols in a systematic way. Nevertheless some of essential data handling for maintenance management are not covered by CMMSs; e.g. planning of data collection, pre-processing of data for analysis, or fixing measurement errors. 111

3 An intelligent software agent is a powerful technology originated from an approach of interaction-based computational model to solve those problems. Software agents are designed to handle autonomous tasks using their intelligence and have capabilities to take initiative, reason, act, and communicate with each other and their operating environment [10]. In the domain of facility maintenance, a multi-agent based remote maintenance support to use an expert knowledge system was proposed by integrating the agents distributed in difference layers by cooperation and negotiation in a global enterprise [11]. In this paper, we suggest five specialized software agents (See Fig. 1). A data collector agent retrieves data from various sources through data interfaces such as internet connection, serial port, and wireless network. A configurator agent is to set up configurations of other agents. For example, the configurations of the data collector agent such as sample sizes to be measured, sampling periods, and message notification types are set by the configurator agent. A recognizer agent diagnoses a status of a facility based on the data which is collected by the data collector agent. A scheduler agent generates work orders for maintenance activities based on the diagnosis results. An analyzer agent conducts reliability assessment to create indicators such as failure rates, mean time between failures, mean time to repair, and expected remaining life time of facilities. Fig. 1. Class diagram; an agent manager and five agent types; configure, data collector, recognizer, scheduler, and analyzer. We developed an MAS based on the design concept and integrated it into a CMMS. In addition, we developed a decision support application to be used by maintenance engineers through the Web. The overall feature of the proposed system is shown in Fig. 2. The system consists of four layers; a manufacturing field, the MAS, the CMMS, and the decision support application. The MAS controls collaboration of the five agents to collect data from the manufacturing field and store them into the CMMS. The data may be converted into indicators so that they ultimately be 112

4 delivered to maintenance engineers through the decision support application. The prototype agent system and the application was developed by C# language based on.net framework. Fig. 2. System architecture: diverse agents collaborate to collect data from sources and reproduce them as worthwhile information for decision-making support. Fig. 3. Decision support scenarios; a use case diagram Fig. 3 depicts a use case diagram of decision support scenarios using the proposed system. When a maintenance manager plans to monitor a facility, he/she may configure data collection and diagnosis. Then the MAS will assign agents and the agents will automatically watch and monitor the status of the facility. If an abnormal condition of the facility is detected, a recognizer agent detects and records it into the CMMS database and sends a message through the decision support application. The 113

5 maintenance engineer can see status of the facility and related reports to find out the root cause of problem. 3 Implementation The proposed system was implemented at an automotive part manufacturing company in South Korea. This factory produces plastic car interior parts such as dashboards, center fascia panels, cup holders, and inner door frames, which are produced by injection molding machines. Thus stabilized operation of the equipment is important to maintain good quality of the plastic products. The factory owns seven large-type machines and eleven middle-type machines. The operation history of the machines had been manually recorded in papers by facility operation managers. At the first step of our project, the operators entered the master profile of the machines such as part list, structure of the parts, and adopted dates. In addition, they entered the history of facility operations from the paper records into the CMMS. Fig. 4. Screen shot; decision support application After the initialization phase, we assigned and activated agents to the machines. Fig. 4 shows a screen shot of the application showing facility condition at operation. A list of currently monitored machines and their conditions are shown. The chart shows a weekly trend of body temperatures of a cylinder by a sensor. A warning message detecting a periodic pattern is shown on the right. Based on the initial operation, we analyzed failure patterns of the machines. Fig. 5. a) shows a time series chart of annual failure frequency of the machines. That the annual failure frequency is stable implies that the probabilistic distribution of life time fits an exponential distribution, which has uniform failure rate over time. Fig. 5. b) 114

6 shows a radio chart representing monthly failure frequency. It shows that the failure rate increases during summer and falls during winter. We guess this seasonal trend may be affected by air temperature; high temperature during summer causes problems to mechanical parts of the machines. Fig. 5. Failure patterns; a) annual failure frequency, b) monthly failure frequency 4 Conclusion In this paper, a multi-agent based intelligent maintenance management system is proposed. We designed software agents for specific purpose of maintenance management, and developed a system by integrating a CMMS and the MAS. The prototype system was implemented in an automotive part industry, and the result represents that complicated information processing can be effectively automated through the MAS. The decision support application produces useful information to maintenance managers and engineers. The future works are expected twofold. Use of statistical analytic methods with the reliability assessment function may be useful to support finding out root causes of failures. In addition, mobile technologies can enhance communications between the system and human workers by faster response and higher mobility in manufacturing field. This will be particularly beneficial to a manufacturing environment which is physically distributed in global locations. References 1. Muller, A., Marquez, A. C., and Iung, B.: On the concept of e-maintenance: Review and current research, Reliability Engineering and System Safety, vol. 93, pp (2008) 2. Faiz, R. B. and Edirisinghe, E. A.: Decision Making for Predictive Maintenance in Asset Information Management, Interdisciplinary Journal of Information, Knowledge, and Management, vol. 4, pp (2009) 115

7 3. Yam, R. C. M., Tse, P. W., Li, L. and Tu, P.: Intelligent Predictive Decision Support System for Condition-Based Maintenance, The international journal of advanced manufacturing technology, vol. 17, no. 5, pp (2001) 4. Tsang, A.: Strategic dimensions of maintenance management. Journal of Quality Maintenance Engineering, vol. 8, issue. 1, pp (2002) 5. Zhou, X., Xi, L., and Lee, J.: Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation, Reliability Engineering and System Safety, vol. 92, pp (2007) 6. Gabbar, H. A., Yamashita, H., Suzuki, K., and Shimada, Y.: Computer-aided RCM-based plant maintenance management system, Robotics and Computer Integrated Manufacturing, vol. 19, pp (2003) 7. Huo, Z., Zhang, Z., Wang, Y., and Yan, G.: CMMS Based Reliability Centered Maintenance, 2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China, pp (2005) 8. Carretero, J., Pérez, J. M., García-Carballeira, F., Calderón, A., Fernández, J., Garcia, J. D., Lozano, A., Cardona, L., Cotaina, N., and Prete, P.: Applying RCM in large scale systems: a case study with railway networks, Reliability Engineering & System Safety, vol. 82, issue. 3, pp (2003) 9. Cheng, Z., Jia, X., Gao, P., Wu, S., and Wang, J.: A Framework for Intelligent Reliability Centered Maintenance Analysis, Reliability Engineering and System Safety, vol. 93, pp (2008) 10. Huhns, M. N. and Singh, M. P., Reading in Agents, Morgan Kaufmann Publishers, San Francisco, CA. 11. Ren, Y. U., Luqing, Y. E. and Chuang, F. U.: A Multi-Agent-Based Remote Maintenance Support and Management System, Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'04), Beijing, pp (2004) 116