Maintenance databases

Automated data acquisition The object of using microprocessor-based systems is to remove any potential for human error, reduce manpower and to automate as much as possible the acquisition of vibration, process and other data that will provide a viable predictive maintenance database. Therefore the system must be able to automatically select and set monitoring parameters without user input. The ideal system would limit user input to a single operation. However this is not totally possible with today s technology. 

Figure 6 shows our plan. According to the HTTR project, HTTR is being operated to accumulate the HTGR operation experience and to prepare the safety and maintenance database. Operational and test results obtained with the HTTR can contribute to the R D programme of HTGR development in other countries USA, France and others. 

Step 4 combines diagnosis and condition indicator status levels and generates an overall risk of failure indication. Typically the worst case condition indicator severity is used to indicate the fluid condition status. Since the diagnosis will relate to a specific maintenance or inspection instruction, this procedure requires a search of the user s maintenance database for previous and scheduled work. Once the maintenance data are known, the fault diagnosis reports the appropriate maintenance response from the users troubleshooting instructions. 

At the early stages of any life extension project, it is very useful to do a short site visit in order to carry out a high-level inspection on equipment, review the documentation, inspection, and maintenance database, speak to the facility personnel, and assess the general condition of the platform and operation. 

Caldeira Duartea, J., Cunhaa, P. and Craveiroc, X, 2013. Maintenance Database. Procedia CIRP, 13 551-556. 

COPE has 9 modules PM scheduler, corrective maintenance, equipment history, equipment reference listing, spare parts entry, database integrity verification, and training. 

The confluence of sharply rising Operations and Maintenance (O M) costs. NRC requested Individual Plant Examinations (IPEs) and increased personal computer capabilities gave rise to the R R Workstation. Its uses and maintains-current PSA models and databases for individual plants to perform O M planning and scheduling, and uses the PSA in IPE models to identify plant design, procedure and operational vulnerabilities. The Risk and Reliability Workstation Alliance was organized by EPRI to support the R R Workshop in order to achieve O M cost reduction, plant productivity and safety enhancement through risk-based, user-friendly, windowed software louls (Table 3.6 8). The Alliance, initiated in 1992, includes 25 U.S. utilities and four international partners from Spain, France, Korea, and Mexico. SAIC is the prime contractor for the R R Workstation, with participation of five other PSA vendors. 

Appendix HI, of WASH-1400 presents a database from 52 references that were used in the study. It includes raw data, notes on test and maintenance time and frequency, human-reliability estimates, aircraft-crash probabilities, frequency of initiating events, and information on common-cause failures. Using this information, it assesses the range for each failure rate. 

Commonly, there are components that are not in any database of failure rates, or the data do not apply for the environment or test and maintenance at your plant. In addition, site specific data may be needed for regulatory purposes or for making the plant run safer and better. For both cases there is a need for calculating failure rate data from incident data, and the mechanics of database preparation and processing. 

The nuclear equipment failure rate database has not changed markedly since the RSS and chemical process data contains information for non-chemical process equipment in a more benign environment. Uncertainty in the database results from the statistical sample, heterogeneity, incompleteness, and unrepresentative environment, operation, and maintenance. Some PSA.s use extensive studies of plant-specific data to augment the generic database by Bayesian methods and others do not. No standard guidance is available for when to use which and the improvement in accuracy that is achieved thereby. Improvements in the database and in the treatment of data requires, uhstaiui.il indu.sinal support but it is expensive. 

Statistical Methods for Nonelectronic Reliability, Reliability Specifications, Special Application Methods for Reliability Prediction Part Failure Characteristics, and Reliability Demonstration Tests. Data is located in section 5.0 on Part Failure Characteristics. This section describes the results of the statistical analyses of failure data from more than 250 distinct nonelectronic parts collected from recent commercial and military projects. This data was collected in-house (from operations and maintenance reports) and from industry wide sources. Tables, alphabetized by part class/ part type, are presented for easy reference to part failure rates assuminng that the part lives are exponentially distributed (as in previous editions of this notebook, the majority of data available included total operating time, and total number of failures only). For parts for which the actual life times for each part under test were included in the database, further tables are presented which describe the results of testing the fit of the exponential and Weibull distributions. 

In addition to addressing the practicalities of database maintenance, we also aim to enhance the range of the facilities available, to make the information within CHIRBASE more readily accessible to users. CHIRBASE contains two form-based applications for query building designed to help novice or expert users to formulate queries the query menu and the automatic search tool. 

In a predictive and reliability maintenance program, it is extremely important to keep good historical records of key parameters. How measurement point locations and orientation to the machine s shaft were selected should be kept as part of the database. It is important that every measurement taken throughout the life of the maintenance program be acquired at exactly the same point and orientation. In addition, the compressive load, or downward force, applied to the transducer should be the same for each measurement. 

Alert and alarm limits The microprocessor should include the ability to automatically alert the user to changes in machine, equipment or system condition. Most of the predictive maintenance techniques rely on a change in the operating condition of plant equipment to identify an incipient problem. Therefore, the system should be able to analyze data and report any change in the monitoring parameters that were established as part of the database development. 

A listing of visual observations. Most of the microprocessor-based systems support visual observations as part of their approach to predictive maintenance. This report provides hard copies of the visual observations as well as maintaining the information in the computer s database. 

The initial database development required to successfully implement a predictive maintenance program will require man-months of effort. The extensive labor required to properly establish a predictive database often results in either a poor or incomplete database. In some cases, the program is discontinued because of staff limitations. If the extensive labor required establishing a database is not available in-house, there are consultants available that will provide the knowledge and labor required to accomplish this task. 

The ideal situation would be to have the predictive systems vendor establish a viable database as part of the initial capital equipment purchase. This service is offered by a few of the systems vendors. Unfortunately, many predictive maintenance programs have failed because these important first critical steps were omitted or ignored. There are a variety of technologies and predictive maintenance systems that can be beneficial. How do you decide which method and system to use ... 

The next step required to establish a predictive maintenance program is the creation of a comprehensive database. 

The labor-intensive part of predictive maintenance management is complete. A viable program has been established, the database is complete and you have begun to monitor the operating condition of your critical plant equipment. Now what ... 

System hardware maintenance also presents some challenges. Trial personnel at the Central location first attempt resolution of hardware malfunctions. If they are unable to resolve the problem, personnel at the central location try to resolve the problem, and if that does not solve the problem a repair order is requested from the vendor as long as there is an active warranty. If the problem persists, the participating site is instructed to ship the device to the central location. If the device is the computer, the site coordinator (SC) is instructed to back up the database before shipping the computer. Personnel... 

Maintenance. The database and software that comprise the data systems must be maintained. 

The diversity of suppliers and the difficulty of maintaining up to date lists made the maintenance of print directories difficult and expensive. Most are now either out of print or have migrated to database format. Nevertheless a review of the older directories is interesting, and they should be retained as they provide valuable background reading. 

Installation qualification (IQ). IQ demonstrates that the equipment/system has been installed correctly at the user site according to vendor standards. The vendor should install the equipment to demonstrate to the buyer that all the components are operating properly. The qualification process includes appropriate documentation of the system components, physical installation and hook-up, and a performance check to verify that the individual components operate and can communicate with each other. System component information, such as serial numbers, type of use, and user performance requirements, should be included in the metrology database for easy tracking and scheduling of maintenance and/or calibration. 

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