# PAL-VMEA: A Novel Method for Enhancing Decision-Making Consistency in Maintenance Management
## 6 Case Study  
Hydroelectric Power Plants (HPP's) are responsible for generating more than 60% of the electric energy that make up the Brazilian energy matrix [55]. The dependence on this energy source, both for the country's industrial development and meeting the needs of modern society, determines the importance of ensuring the operational availability and reliability of the power generation system, aiming at the continuity of energy supply. In this way, the maintenance management of HPP's assets must promptly adapt to meet the high standards of availability and reliability necessary to comply with the energy production and dispatch schedules. When a fault affects the main functions of a hydrogenerator, it may result in the reduction and/or interruption of the generation capacity of the power plant. In these cases, penalties and/or high fines established by regulatory agencies may be attributed to the power plant generating company due to noncompliance with demand. The weights established using the PAL-VMEA method aim to reduce the inconsistency in the information used to obtain the VRPN indicator. As this indicator reflects the equipment's operation, maintenance, and risk to assets and employees' safety, as well as how much the interconnected electric system can be impacted by the failures in the analyzed mechanical systems (considering the reliability and operational availability of the equipment), consistency is a key point in this decision process. As a practical application of the proposed method, a case study is presented considering a Kaplan turbine hydrogenerator. The unit belongs to a run-of-the river power plant located in the Brazilian North region. According to its layout, a guide journal bearing is positioned near the turbine, two guide journal bearings are placed near the generator (one above and one below) and one thrust journal bearing is located near the shaft mid-span. The generated power output is around 150 MW. A simplified functional tree, covering the three main mechanical systems of the hydrogenerator, as well as the respective and most significant subsystems, is shown in Figure 5.  
From the chosen subsystems, eleven KPCs were considered for the VMEA analysis:  
1. Oil pressure on the air-oil accumulator
2. Response time for the Kaplan mechanism
3. Water linear momentum for the water intake
4. Transmitted torque for the turbine blades  
Figure 5: Analyzed system simplified functional tree. * Control oil flow for the Kaplan head
* Flow rate for the lubrication system
* Oil pressure for the lubrication system
* Lift force for the journal bearings
* Angular moment for the shaft and joining elements
* Oil outlet temperature for the heat exchanger
* Transmission force for the servo motor actuator  
Seven necessary skills were listed to assess the impact of failure modes on the variation of the eleven KPCs: reliability and risk analysis, maintenance management, product quality management (materials and manufacturing process), failure mechanisms analysis, professional experience in hydroelectric power plant, electrical and automation knowledge, and lubrication, hydraulics, and pneumatics knowledge. Four experts participated in the interviews and in the evaluation and analysis process for each KPC. Together, these specialists have almost 100 years of experience in product design, reliability and risk analysis, quality management, and rotating machinery monitoring and diagnosis, among other abilities. From the application of BWM, the weight distribution of the skills of each decision-maker was obtained. Table 7 shows the result of applying this method to define the weight of skills for each decision-maker. Note that the sum of the knowledge weights of each evaluator is equal to 1. It is worth mentioning that the method does not limit the number of participating experts and, although the time consumed by the analysis increases, a greater number of participating specialists tends to increase the accuracy of the results.