Fusion of CMMS Data and CM Data A Real Need for Maintenance
Maintenance can be considered as an information processing system. Therefore, the development of future maintenance information systems is one of the most important current research problems to model the effects of automatic condition monitoring systems enabled by embedded electronics and software.
With the emergence of intelligent sensors for measuring and monitoring the health state of a component and the gradual implementation of Information and Communication Technologies (ICT), the conceptualization and implementation of e-maintenance is becoming a reality. Today there are two main systems implemented in maintenance departments, Computer Maintenance Management Systems (CMMS) are the core of traditional maintenance record-keeping practices and Condition Monitoring Systems (CM) which are capable of directly monitoring asset components parameters. However, the attempts to link observed CMMS events to CM sensor measurements have been fairly limited in their approach and scalability.
During the last couple of decades the global competition and advancement of ICT have forced Production and Process Industries through a continuous transformation and improvement process. The business scenario is focusing more on e-business intelligence to perform transactions with a focus on customers’ needs for enhanced value and improvement in asset management. Such business requirements compels the organizations to minimize the production and service downtime by reducing the machine performance degradation.
The above organizational requirements necessitate the development of proactive maintenance strategies to provide optimized and continuous process performance with minimized system breakdowns and maintenance. With these changing systems of the business world in the 21st century a new era of e-intelligence, e-factory, e-automation, emaintenance, e-marketing and e-service have emerged.
A Need Beyond an ICT Gateway
e-Maintenance provides the organization with intelligent tools to monitor and manage assets (machines, plants, products, etc.) proactively through ICT, focusing on health degradation monitoring and prognosis instead of fault detection and diagnostics.
Figure 1. ICT architecture in maintenance.
Maintenance effectiveness depends on the quality, timeliness, accuracy and completeness of information related to machine degradation state. This translates into the following key requirements: preventing data overload, ability to differentiate and prioritize data (during collection as well as reporting) and to prevent as far as possible the occurrence of information islands.
While the use of a good version of either technology can help to achieve the maintenance goals, combining the two into one seamless system can exponentially increase the positive effects to a maintenance group’s performance. Only a few years ago the idea of linking CMMS and CM technologies was mostly only a vision or at best too expensive. With the technology available today it is relatively easy and inexpensive to combine the strengths of a top-notch CMMS with the wizardry of a leading-edge CM system so that work orders are generated automatically based on the information provided by CM diagnostic and prognosis capabilities.
A top-shelf CMMS can perform a wide variety of functions to improve maintenance performance and it is the central organizational tool for World-Class Maintenance (WCM). CMMS is primarily designed to facilitate a shift in emphasis from reactive to preventative maintenance (PM) by allowing the maintenance professionals to set up an automatic PM work order generation. CMMS can also provide historical information which is then used to adjust the PM system setup over time to minimize repairs that are unnecessary, while still avoiding run-tofailure repairs. PM for a given piece of equipment can be set up on a calendar schedule or a usage schedule that utilizes meter readings. A fully-featured CMMS also includes inventory tracking, workforce management and purchasing in a single package that stresses database integrity to safeguard vital information. The final result is optimized equipment up-time, lower maintenance costs and better overall plant efficiency.
On the other hand the CM system should accurately monitor the real-time equipment performance and alert the maintenance professional of any changes in performance trends. There are a variety of measurements that a CM package might be able to track and the very best ones are expert systems that can analyze measurements like vibration, and diagnose machine faults. Such expert system analysis will put maintenance procedures on hold until absolutely necessary, thus extracting maximum equipment up-time. In addition, the best expert systems offer diagnostic fault trending where individual machine fault severity can be observed over time.
Both CMMS and CM systems have strong benefits that make them indispensable to maintenance operation improvements. CMMS is a great organizational tool but cannot directly monitor equipment conditions whereas a CM system excels in monitoring those conditions but is not suited to organizing your overall maintenance operations. The logical conclusion is to combine these two technologies into a seamless system which helps to avoid catastrophic breakdowns and eliminates needless repairs to equipment that is running satisfactorily.
The general opinion among the maintenance staff is that the application of information technology brings dramatic results in machine reliability and maintenance process efficiency, however only few maintenance managers can show or calculate the benefits of such applications. Technology providers are trying to develop more and more advanced tools while the maintenance departments seem to struggle with the daily problems of implementing, integrating and operating such systems.
The users combine their experience and heuristics in defining maintenance policies and in the usage of condition monitoring systems. The resulting maintenance systems seem to be a heterogeneous combination of methods and systems in which the integrating factor between the information and business processes is the maintenance personnel. The information in the maintenance systems goes through these human minds forming an organizational information system and creating a high reliance on the expertise of the maintenance staff.
Data of Different Nature
CMMS use context-specific textual data to record information such as asset load and usage, component failures, servicing or repairs, and inventory control. The underlying structure is typically heavily regulated, allowing for a large base of consistently structured data. These systems are the core of traditional scheduled maintenance practices and rely on bulk observations from historical data to make modifications to regulated maintenance actions.
Typically CM systems collect componentspecific quantitative data to assist maintenance crews in the identification of failures which are imminent, or have already occurred, and they utilize a large number of electronic sensors in combination with a highly-specialized data acquisition system. Currently, there is no standardization in the way that data is collected across platforms or vendors, primarily because the technology has not been in use long enough to have fully matured. There is still much investigation and debate on what information is required for asset health diagnostics and how that information is used to meet CBM objectives
An Integrated Approach Asset Management
CMMS and CM must be linked. The measurements and analysis made by a CM package must be available to maintenance planners who work with a CMMS for the purpose of scheduling predictive and other types of work orders. In the past, maintenance organizations that used both CMMS and CM technologies linked the two systems by inputting CM data manually into the CMMS. This is an acceptable way to transfer data for the purpose of scheduling predictive maintenance work orders, but it is time-consuming.
Another recently used CM data transfer method is passive data exchange which involves writing pertinent CM data to a specified local or network directory. The data to be exchanged includes equipment identification, time stamps, repair priority, repair recommendations and observations. The CMMS programme routinely checks this directory and if a transfer file is found the CMMS reads it and imports it into the database. Historically this method of data transfer has been very specific to formal cooperation between various manufacturers of CM and CMMS software. The passive data transfer method is better than manual data entry but still falls well short of the total automation and instant access to information which is possible when the CMMS and CM program are fully integrated.
So far integration has been addressed largely from the view point of representing the collected information to the end-user (operator or manager) in an effective manner, i.e. bridging the gap between information from plants and equipment and the enterprise resource planning (ERP) platforms.
A major initiative has been the development of information integration specifications to enable open, industry-driven and integrated solutions for Asset Management. Some current efforts to standardize the emaintenance platforms are: Machinery Information Management Open Systems Alliance (MIMOSA) [2], GEM@WORK [6], CASIP [1] and PROTEUS [5]. Such platforms provide an information schema at the application level and an application programming interface (API) to communicate with the underlying protocol stack.
To our knowledge the existing communication technologies are not well-suited for reliable and timely delivery of appropriate data between distributed end systems in industrial environments. In our opinion this remains to be the critical missing link in the seamless integration vision.
Integration of Data Sources
The first step to integrate a CMMS and CM package into an automatic system is setting up the way for them to communicate. This is done by setting up consistent data in each system which will allow them to communicate using common base information. For example all equipment monitored by the CM system must also exist in the CMMS database and must be called by the same name in both systems.
Figure 2. Existing integration of CM/CMMS. Cortesy of MRO Spain.
Next, there must be a system of data cross-references between the sensors, meter tags or other measurement tools in the CM system and the appropriate module in the CMMS that associates readings in one system with readings in the other. Meter readings or alarm triggers that are out of the accepted range in the CMMS should trigger a predefined work order. Any discrepancy in this cross-reference for a piece of equipment will nullify the link for that device making the ability to predict problems much less comprehensive, this makes the upfront planning of data entry rules and database setup a critical part of the pre-integration process.
The third step in fully integrating a CMMS and CM package is to provide a direct link between the systems’ data tables which is referred as “active exchange” of data. In today’s environment, the best CMMS databases feature open architecture such as SQL, Oracle and others. These CMMS databases can be read from and written to by CM programmes with certain capabilities.
The most obvious obstacle in the integration of CMMS and CM data is the disparate nature of the data types and attempts to remedy this problem have been met with inconsistent implementation and limited scalability. The first such technique is to assign the mostly qualitative CMMS data with quantitative indexing, allowing for CM data to be separated into discrete maintenance states. It is the data administrator’s responsibility to correctly insert the appropriate fault or work code into the maintenance logs, which to date has not been done with sufficient accuracy or consistency to be deemed reliable.
A more recent approach is to simply embed CM files into CMMS records or vice versa (Figure 2). This small degree of integration allows the maintenance people to launch work orders related to predictive inspections, to do the tracking of these WO’s and visualize historical data. We can conclude that this is a basic attempt to integrate predictive maintenance tasks in the complex maintenance schedule.
Figure 2 shows vibration data and collection routes as a part of a CMMS. Tasks related to data collection are integrated in the schedule and users can review historical data with a client application.
However there is no integration between the databases in the process to produce a more accurate decision support system and the embedded files can only be opened in their original client application providing limited opportunities for data mining or other investigations. Other necessities to link these data types include real-time analysis and historical case study matching. Typically in CM manufacturers’ interest this is done more for system validation purposes and requires large amounts of manual investigation of the data.
The second part of this article will be published in the next issue of MaintWorld magazine.
»»References 1. Baptiste J., A case study of remote diagnosis and e-maintenance information system, e-Proceedings of Intelligent Maintenance System, 15-17 July, 2004, ArIes, France. 2. Kahn, J and Klemme-Wolf, H (2004), Overview of MIMOSA and the Open System Architecture for Enterprise Application Integration, Proceedings of the 17th European Maintenance Congress, pp. 333-341, May 2004. 3. Manning, C. and Schutze , H. Foundations of Statistical Natural Language Processing. MIT 1999. Press, Cambridge, MA. 4. Parida A. and Kumar U., Managing Information is key to Maintenance Effectiveness,e- Proceedings oflntelligent Maintenance System, 15-17 July, 2004, Arles, France. 5. Thomas B., Denis R., Jacek S., T. Jean- Pierre and Z. Noureddine, PROTEUS – An Integration Platform for Distributed Maintenance Systems, e-Proceedings of Intelligent Maintenance System’ (IMS’ 2004), 15-17 July, 2004, ArIes, France. 6. Vouk M. A., 2008. Cloud computing Issues, research and implementations. In 30th International Conference on Information Technology Interfaces (ITI 2008). Cavtat/Dubrovnik, Croatia, June 2008, 31-40. 7. Wang X., Liu C. and Lee J., Intelligent Maintenance Based on Multi-sensor Data Fusion to Web-enabled Automation Systems, e-Proceedings of Intelligent Maintenance System, 15-17 July, 2004, Arles, France.
