Welding station condition monitoring using bluetooth enabled sensors and intelligent data management

Transcription

1 Journal of Physics: Conference Series Welding station condition monitoring using bluetooth enabled sensors and intelligent data management To cite this article: D R Eyers et al 2005 J. Phys.: Conf. Ser Related content - A water-powered Energy Harvesting system with Bluetooth Low Energy interface M. Kroener, K. Allinger, M. Berger et al. - Self-activating intelligent home using bluetooth P Karthikeyan, N Sumanth and S Jude - Bluetooth Low Energy (BLE) Based Power Window System J Purba and D Wahyudin View the article online for updates and enhancements. This content was downloaded from IP address on 11/11/2018 at 12:47

2 Institute of Physics Publishing Journal of Physics: Conference Series 15 (2005) doi: / /15/1/024 Sensors & their Applications XIII Welding station condition monitoring using bluetooth enabled sensors and intelligent data management D.R. Eyers, R I Grosvenor and P W Prickett Intelligent Process Monitoring and Management (IPMM) Group, Cardiff School of Engineering, Cardiff University, Wales Abstract. This paper reports on the first phase deployment of bluetooth enabled condition monitoring systems at a large multinational engineering company. The radio networking of sensor signals is a fast developing area and the facilities afforded by the Wisnet device were used in the monitoring of a welding station. This and any of the planned further monitoring systems had to comply to a carefully managed IT information plan at the company. For the example application, the development and testing of microcontoller-based pre-processing of data is reported. This includes further development of the Petri Net approach to provide event-based monitoring as a sensible alternative to the continuous transmission of the sensory data. 1. Introduction The established Intelligent Process Monitoring and Management (IPMM) research group has evolved Petri Net-based approaches to machine and process condition monitoring. The importance of the intelligent use of captured data and the increasing emphasis on remote monitoring were first established during the MIRAM project [1]. More recent activities have included a European Regional Development Fund (ERDF) project targeted at delivering monitoring systems to SME s located in south Wales [2]. Achievements stemming from this project and related research include the successful deployment of distributed, networked microcontroller-based remote monitoring systems. Such systems are configured for a particular application via the coding of the Petri Net models into the microcontroller devices, as reported by Frankowiak [3]. In wider context, modern process monitoring systems are capable of observing hundreds of simultaneous sensor readings, and of returning these, often via various networks and/or the internet, to a central location for processing and storage. In the absence of a carefully considered Information Plan, the volume of data produced can become unmanageable for both the data transmission mechanism and the computational process and data storage equipment. The multinational engineering company involved with the research reported here, have high end capabilities with their LAN infrastructure. Although the acquired sensor signals for the welding station monitoring application would represent inconsequential traffic across the LAN, they considered the need for effective data management to be vitally important IOP Publishing Ltd 143

3 144 Welding Station Monitoring The selected application used to evaluate the monitoring system was a welding station. This is used almost continuously within a 3 shift pattern as one of the production stages for one of the company s core products. The welding of 2 end pieces to a flexible tube is performed automatically by the robot welder. The loading and unloading of the welder is a manual operation and one cycle consists of loading the assembly, welding of one end, unloading-turning-around and reloading, welding of the second end and unloading/transfer. The production profile may be conveniently be monitored by utilising the welding current measurements on the robot welder. There is clear distinction between the idle state and welding currents and a typical profile is shown in Figure 1. arbitrary units 12 Welding Current (arbitrary units) :24:28 10:24:37 10:24:46 10:24:55 10:25:04 10:25:13 10:25:22 10:25:31 10:25:40 10:25:49 10:25:58 10:26:07 10:26:16 10:26:25 10:26:34 10:26:43 10:26:52 10:27:01 10:27:10 10:27:19 10:27:28 10:27:37 10:27:46 10:27:55 10:28:04 10:28:13 10:28:22 10:28:31 10:28:40 10:28:49 Time Figure 1. Typical welding station operating profile Bluetooth Sensor Communications A review of the data link issues for the monitoring application was performed [4]. The company LAN was audited and as it is based on a Fast Ethernet (100Megabit) backbone it is a high-end specification and would easily cater for the current and future monitoring applications. The review also considered higher speed communications (Gigabit and high-end wireless networks for example) as well as lower speed communications via HSCSD, GPRS and other mobile communications. The selected approach was to use, the now, commercially available radio-linked sensor acquisition modules. The Wisnet [5] solution consists of an acquisition/transmission node that may be connected to up to 4 sensors and a bluetooth network controller (BNC). The bluetooth system is capable of transmitting at distances up to 100 metres (Bluetooth Class 1) and achieves data capture by means of continuous sampling for defined intervals. The base sampling rate is predetermined by the system supplier at installation and may be configured relative to this by the user. The resulting data resolution was set as a compromise between the system capabilities and the need for sensible data volumes. In other applications the

4 145 Wisnet is typically set with a 1 second sample interval. For the robot welding station a nominal 3 second sample interval was selected. In operation, the Wisnet system captures a sensor input and records this instantaneous sampled value to the local acquisition and transmission station. This facilitates the local storage of up to 24 sampled values before transmission to the BNC. The BNC was housed on the shop floor and was connected to the IT department via an ethernet connection to the company server. Testing of the installed system, operating in its supplied and configured mode suggested that the daily data traffic and storage would be of the order of 500 Kbytes. Intelligent Data Management The considerations that have led to a 99% reduction in the daily data traffic were structured in line with the knowledge derivation process proposed by Santoso & Lamoree[6]. Thus the defined stages that were considered were: (i) data preparation, (ii) extraction of information from stored data, (iii) information assimilation and (iv) report presentation. Figure 1 shows some acquired data obtained from the robot welding station, for the production of 2 units (4 welds, 2 manual product reversals), with the original continuous sampling regime. In this configuration at least 32 sample values travelled across the network. The Wisnet system has no intelligence to recognise extended periods of waiting for components to be loaded or machine downtime, for example. If effect the system once configured will blindly continue to send data to the BNC and server. What was actually required was the start time and stop time for each cycle of welded units. The main technique that was to be employed for tracking the station performance was overall equipment effectiveness (OEE). The constituent parts of the OEE calculation, availability, performance rate and quality rate were also deemed to be more efficiently obtained with an adaptation of the continuous monitoring approach. Thus the need for a pre-processing stage of the sensor inputs to be connected to the Wisnet units was identified. It was decided that the data preparation and information extraction should become event-based. This was achieved by the introduction of a microcontroller to pre-process the continuous sensor data as required. The deployed pre-processing microcontroller operated in a similar way to a state machine and used an adapted version of the Petri Net modelling for embedded microcontrollers technique developed within the IPMM group [3]. Thus for the robot station example, the pre-processing stage detected a change of state (for example, waiting-to-welding) and acted in an interrupt mode (permitted by the alarms condition in the Wisnet software) with the Wisnet transmitter. With the data traffic then reduced to start of cycle and end of cycle times the 99% reduction in traffic (500Kbytes per day reduced to 5 Kbytes per day) was achieved. The Petri Net approach will allow flexibility in future monitoring deployments, in that the configurations required for particular applications can be implemented via the programming of only the Petri Net model sections of the microcontroller code. Information Assimilation and Report Presentation These important aspects of the monitoring regime were also fully considered. The adaptation of the acquisition end of the bluetooth sensor monitoring system reported in the preceding section in turn led to a requirement to customise the data handling software at the database/server end of the system. With the supplied configuration, for example, after processing (and potentially batching) of the data, it is eventually transmitted to a central server, where it resides as a text file (one file for each sensor for

5 146 each day). Data analysis to generate meaningful information, for the new context was then found to be cumbersome. The company user requirements, for example, dictated the need for multiple days information, or combined sensor information. To resolve this issue, the raw text files are now read by a sever-side agent, and written to a Microsoft SQL Server 2000 database. This database affords ODBC in addition to a number of other proprietary database technologies, and facilitates querying to industry standard SQL-92. When implemented in a networked environment, this database allows full distributed data access both to clients on a LAN or WAN, or using protocols such as HTTP for Intranet or Internet. After the database was populated with production data it was possible to use a wide variety of tools to extract information. Direct querying in SQL Server Custom written applications to display information o In daily reports o As live data screens in the production environment o On Intranet/Internet pages Using Office Application tools for further processing o Microsoft Excel o Lotus 123 Information presentations tools such as Microsoft Crystal Reports Depending on the information requirement, each of these techniques has its relative advantages. Within the particular company, Crystal Reports has been implemented to automatically produce daily reports, and with custom programming these reports are packaged with the Adobe PDF file format and automatically sent by to production managers & team leaders. The shift, daily and historical performance data was also made accessible to the appropriate personnel via dynamic web pages. An example output is shown in Figure 2. The dynamic WebOEE page presents a headline value for the calculated OEE performance indicator value for the selected day. This is usually used to track the welding station performance for the current interval. Historical data can also be accessed via selection from the calendar window. The shift pattern details and the tabulated and charted production values are then automatically displayed. The selected example shows that shift 2 only produced 29 welded units and experienced significant downtime due to the unavailability of materials. The WebOEE page also provides a link to a monthly summary. An example summary for March 2005 is shown in Figure 3. During this period the 3 shift pattern was in operation only during weekdays. There were also gaps in the shifts worked on the 15 th /16 th March and from 25 th March onwards (Easter holidays). This data was collected during the deployment and evaluation stage of the system. As evident from Figure 3 the monitoring system confirmed the relatively large fluctuations in the robot welding operations from day to day and from shift to shift. This was key information for the production management and led to a number of actions. These included the agreement to extend the implementation of the bluetooth sensor monitoring to other production operations and machines and to develop interfaces to other production performance systems.

6 147 Figure 2. Main web-based display of production performance. Discussion The reported work represents a considered and useful deployment of the now commercially available radio/bluetooth enabled sensor monitoring systems The additional efforts to provide some preprocessing of the captured data and to make the system event-based led to significant savings in terms of the data traffic. The use of dynamic web pages and the adoption of the OEE measure would appear to be a sensible choice. A good balance is struck between providing up-to-date useful information whilst avoiding information overload.

7 148 Figure 4. Monthly performance chart. Acknowledgements The support of the multinational engineering company, who are not explicitly named here, for the parttime MPhil studies of D.R. Eyers (within the IPMM research group) is acknowledged. The on-going collaboration with Expert Monitoring Ltd, the suppliers of the Wisnet system is also acknowledged. References [1] Drake P R, Grosvenor R I and Jennings A D 1995 Proc. Sensors and their Applications VII, Dublin [2] [3] Frankowiak M.R P.hD Thesis, Cardiff School of Engineering [4] Eyers D.R Internal document, Cardiff School of Engineering [5] [6] Santoso S. and Lamoree J IEEE Power Engineering Society Summer Meeting, Seatle