Plant data collection

The task remains to determine the mean annual enthalpy from plant physiognomy. An analysis is presented relating foliar physiognomic characters to mean annual values of enthalpy, temperature, specific humidity, and relative humidity that exploits the method and data in the Climate-Leaf Analysis Multivariate Program (Wolfe 1993). From present-day plant data collected from North America, Puerto Rico, and Japan, the leaf parameters are searched for linear combinations of the foliar characteristics that covary with the local climates. By doing so, the foliar characteristics can be determined that covary with one another and which best correlate with climate parameters. 

To achieve its goals, a PSM must be calibrated on plant data collected during special organised test-runs . Additional data reconciliation programs may be used to increase the reliability of the plant data, by minimising the errors in measurements and supplying estimations for non-measured variables. 

An example of an existing computerized data source for nuclear power plants is the Nuclear Plant Reliability Data System (NPRDS) of the Institute of Nuclear Power Operations [6]. Another example of power plant data collection and record keeping programme is given in Ref. [8]. 

The data presented here are typical values only. Actual data needs to be collected. The data given here in Table IX/1.0.7-1 is for the reader to get an idea about the issue. Major sources of these data are from Refs. [1] and [3], and balance data are from the plant data collected. 

The same general comments hold as for Unit 3. Figure 7 provides an example of the AE monitoring data collected from 19.06.97 to 16.07.97, in terms of the main plant parameters vs time (fig. 7a), as well as of the AE RMS values (fig. 7b). 

CCPS G-56. 1998. Guidelines for Improving Plant Reliability through Data Collection and Analysis. American Institute of Chemical Engineers, Center for Chemical Process Safety, New York. 

The monitoring software for every system will be different. However, all software is there to achieve one goal—it must gather data, ensure that it is correct, and then analyze and diagnose the data. Presentations must be in a convenient form and should be easily understood by plant operational personnel. All priorities must be to the data collection process. This process must not in any manner be hampered since it is the corner stone of the whole system. 

Guidelines for Improving Plant Reliability through Equipment Data Collection and Analysis (1998)... 

I9f.s- 975 Commercial Power Failure frequency collection Sy.slemaiic data collection ir plant.s reports... 

IPRDS prepares nuclear plant data under the auspices of the ANSI/Failure and Incidents Reports Review (FIRR) Data Subcommittee. Data collection teams visit plants to e) n... 

Setting up a Data Collection System in a Chemical Plant (6.10)... 

The model of human error held by management and the plant culture constitutes the environment in which the data collection system operates. Within this environment, all data collection systems need to address the topics listed in Figure 6.1. These topics, from the types of data collected, to the feedback systems that need to be in place, will be addressed in subsequent sections of this chapter. 

The type of data collected on human error and the ways in which these data are used for accident prevention will vary depending upon the model of error and accident causation held by the management of an organization. This model will also influence the culture in the plant and the willingness of personnel to participate in data collection activities. In Chapters 1 and 2 a number of alternative viewpoints or models of human error were described. These models will now be briefly reviewed and their implications for the treatment of human error in the process industry will be discussed. 

Consideration of how formal data collection incident investigation methods are to be introduced into a plant in order to ensure acceptance and long-term support by the workforce... 

Workforce Support for Data Collection and Incident Analysis Systems Few of the incident investigation and data collection systems reviewed provide any guidelines with regard to how these systems are to be introduced into an organization. Section 6.10 addresses this issue primarily from the perspective of incident reporting systems. However, gaining the support and ownership of the workforce is equally important for root cause analysis systems. Unless the culture and climate in a plant is such that personnel can be frank about the errors that may have contributed to an incident, and the factors which influenced these errors, then it is unlikely that the investigation will be very effective. 

SETTING UP A DATA COLLECTION SYSTEM IN A CHEMICAL PLANT... 

In previous sections of this chapter, the required characteristics of effective causally based data collection systems to reduce human errors and accidents have been described. In this final section, the stages of setting up such a system in a plant will be described. 

Implement Pilot Data Collection Exercise in Supportive Culture In order to ensure that the data collection system has been thoroughly checked and tested prior to its laimch, it is advisable to test it in a plant or plant area where there is likely to be a supportive culture. This will allow the effectiveness of the system to be addressed prior to a larger-scale implementation in a less controlled environment. 

Plant-specific data are frequently unavailable or are low in their level of confidence. Further, this source of data cannot provide information on equipment not in use at the plant, nor can it do more than suggest how plant equipment might behave under different circumstances. Since data collection is very difficult, using shared or generic data is one way of resolving these problems without the expense of extensive data collection systems. 

Drago, J. P., Borkowski, R. J., Pike, D. H., and Goldberg F. F. The In-Plant Reliability Data Base for Nuclear Power Plant Components Data Collection and Methodology Report. NUREG/ CR-2641, ORNL/TM-9216, January 1985. 

The Swedish Thermal Power Reliability Data System (ATV) is maintained and managed by the Swedish State Power Board at Stockholm, Sweden. Engineering and reliability data have been collected from both nuclear and nonnuclear power generating plants. Nuclear data collection began in 1973. Collection of reliability data began in 1976. Over 30,000 events have been recorded in the data base. 

Three reports have been issued containing IPRDS failure data. Information on pumps, valves, and major components in NPP electrical distribution systems has been encoded and analyzed. All three reports provide introductions to the IPRDS, explain failure data collections, discuss the type of failure data in the data base, and summarize the findings. They all contain comprehensive breakdowns of failure rates by failure modes with the results compared with WASH-1400 and the corresponding LER summaries. Statistical tables and plant-specific data are found in the appendixes. Because the data base was developed from only four nuclear power stations, caution should be used for other than generic application. 

Appendix III of this report provides a detailed description of the reliability data used in event tree and fault tree quantification. Because of its extensive operating experience and the uniqueness of the BRP design, BRP plant-specific data was used whenever possible. Plant-specific data sources included plant maintenance orders, control room log books, surveillance tests, LERs, event reports, deviation reports, plant review committee meeting minutes, and USNRC correspondence. The plant-specific data used spanned the period from 1970 to 1979. Data before 1970 did not include maintenance orders or surveillance tests and therefore were excluded. The plant-specific data collected for BRP is presented in detail in Appendix XIII. Table III-4 summarizes 30 plant-specific component failure rates and Table 11-06 contains plant-specific maintenance unavailabilities for 20 components. These tables are a summary of the BRP component failure and maintenance outages. 

WASH-1400 is a fundamental document for PRA methodology. The data appendixes contain a great deal of useful information on methods of data assessment. A large number of sources for data are considered, and very general failure rate estimates will produce only gross approximations. Since the advent of data collection schemes across and within plants, the WASH-1400 data are solely useful as a constituent to a data aggregation process or as widely bounded figures that provide a basis for comparison. 

It should be noted that the data collection and conversion effort is not trivial, it is company and plant-specific and requires substantial effort and coordination between intracompany groups. No statistical treatment can make up for inaccurate or incomplete raw data. The keys to valid, high-quality data are thoroughness and quality of personnel training comprehensive procedures for data collection, reduction, handling and protection (from raw records to final failure rates) and the ability to audit and trace the origins of finished data. Finally, the system must be structured and the data must be coded so that they can be located within a well-designed failure rate taxonomy. When done properly, valuable and uniquely applicable failure rate data and equipment reliability information can be obtained. 

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