Coal

SAP Enhancements Improve Coal Plant Maintenance Practices

Issue 2 and Volume 112.

In the search for improved physical asset management performance, a major Canadian-based power generation company wanted to leverage its investment in its SAP PM Computerized Maintenance Management System (CMMS). Its goal was to develop good reliability and maintenance information practices at its large coal-fired plant consisting of six 400 MW units.

By interfacing its SAP work order module with a system that collects and analyzes maintenance and reliability information, the generation company’s maintenance engineers and technicians can now analyze historical data. This, plus the adoption of a common language for potential and functional failures, is leading to improved maintenance, inspection and decision-making techniques. In turn, this is resulting in increased reliability, fewer failures and reduced maintenance costs.

The key project goals were:

  • Improve maintenance strategies within the plant’s maintenance department
  • Bridge the gap between the SAP PM software and the data required for high-quality reliability analysis using tools such as Weibull, Pareto, Jack-knife, EXAKT and others
  • Propose a “knowledge model” to empower maintenance personnel
  • Fully exploit information derived from experience to reduce costs and increase reliability.

The project stemmed from frustrating experiences of technicians and engineers over the lack of a fundamental and consistent maintenance information model within the work order process. For one thing, they were never certain what information should be included when closing a work order. For another, they were not sure what use, if any, would be made of the observations and comments they provided. And finally, they lacked a consistent way to express their thoughts within the structure and form of the CMMS work order.

Methodology

The utility company worked with Canadian-based Optimal Maintenance Decisions Inc. (OMDEC), which helps businesses identify and predict critical equipment failures that affect their ability to effectively deliver their products or services. OMDEC uses a process it developed to calculate a company’s cost of failure, define its probability of failure within a given timeframe and measure the statistical confidence levels.

OMDEC’s first step was to examine the SAP database to determine whether the data required for high quality reliability analysis was available. Next, it extracted the data that contributes to building a reliability knowledge base. It then reviewed the data for consistency, adequacy and completeness. OMDEC made corrections and adjustments to compensate for any lacking data; it recommended the power generator revise its procedures to ensure that the right data was collected in the right format at the right time.

OMDEC’s final step was to apply the results to EXAKT to predict failure probabilities within a specified time frame. (EXAKT is a statistical tool that establishes the correlation between the condition variables, the working age of the unit and the cost-risk profile to show the risk and probability of failure before the next scheduled outage.) For the data extraction process, OMDEC’s software product called REWOP (Reliability Engineering Workbench Optimizer) was interfaced with the SAP work order module.

The REWOP system collects valuable information from data stores across an organization. It assembles that data in a form that links directly with reliability analysis (RA) software or the reliability centered maintenance (RCM) system. Maintenance personnel have a method to analyze data directly from the CMMS without having to export and manipulate data externally. REWOP enhances the data’s value and allows the company to use formerly unused reliability analysis software, placing the analysis results at the disposal of maintenance engineers and managers.

Interfacing the systems was a straightforward process. However, to accommodate the missing data in SAP, a couple of additional fields were added. Their purpose is to facilitate cross reference to the RCM database and collect significant data related to potential failures, functional failures and suspensions.

The linking of REWOP to SAP allowed the generating company to perform several key functions, such as:

  • Provide a hot link between CMMS and RCM so that the RCM failure mode data can be inserted into the work order
  • Ensure that RCM records can be updated using the actual experience from the maintenance action—or in the absence of an RCM record, REWOP creates one
  • Prepare a reliability knowledge base for use by the analysis tools
  • Provide an automatic test of its own accuracy and responsiveness by comparing actual results to predicted results.

Results

The initial data analysis confirmed that the generating company was typical of many in that its source data was incomplete, missing and inconsistent and thus not easily analyzed. It had no standard procedures and guidelines for collecting the “as-found” condition of assets in a format appropriate for reliability analysis. In addition, the generating company’s key data was embedded in text and company-specific terminology was widely used; therefore data mining techniques were based on word associations. No maintenance information model existed on which to build an analyzable database. As a result, no meaningful reliability analysis could be performed.

Because of gaps in the information practices, analytical tools could not be reliably used. It was difficult—if not impossible—for the maintenance department to perform rigorous analyses to arrive at conclusive maintenance decisions.

Applying the REWOP methodology, the maintenance department recognized a better way of organizing their work orders for useful historical data retention and analysis. The advantages of separate work orders for each significant unique item-function-failure-cause combination became apparent and formed a basis for their reliability-centered knowledge database.

Next, the company was able to perform reliability analyses and established the clear relationship between a component’s working age, its condition and its failure probability. This enabled it to turn the static historical work orders into a rich source of data for maintenance and reliability improvement. Applying REWOP principles, the company’s maintenance team now analyzes its maintenance data to produce usable intelligence regarding the effectiveness of current maintenance practices and policies.

As a further benefit, the generating company sees that these procedures reduce the clerical workload and at the same time lead to improved asset performance as a result of good reliability analysis.

In examining the degree of change that will be required for these improvements, the company saw that the only significant requirement for the use of REWOP was that maintenance personnel (including technicians, planners, engineers and supervisors) use standard RCM terminology to record their field observations on the work order form. This resulted in the “added value” benefit that—at last—everyone was talking the same language.

The company now evaluates what it learned in order to build an action plan for broader maintenance improvement. These key actions include introducing simple and effective documentation standards and procedures that promote good, complete data on the work order; fully implementing the REWOP system and its methodology; introducing RCM language in day-to-day work order documentation; ensuring quality of growing reliability knowledge base; adopting the use of reliability analysis tools on a broader basis; and carefully measuring the results from the reliability analysis.