Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility †
Abstract
:1. Introduction
2. Planning for Testing Based on the Avoided Risk Criterion
- avoided risk value—;
- outlays made to reduce risk—;
- other risk value—.
- —cash flows related to costs of repairs, diagnostic testing or replacement in year t;
- —probability of failure in the period prior to diagnostic testing, repairs or replacement;
- —probability of failure in the period after diagnostic testing, repairs or replacement;
- —discount rate;
- —year index;
- —total planned service life (years);
- —year in which the element is tested, repaired or replaced.
- defining hazardous scenarios, selecting the most hazardous scenario;
- working out a model of the development of wear processes, determining the failure criterion;
- calculating the change in the element failure probability;
- optimizing the times of maintenance activities that ensure maximization of the NPV index.
3. Turbine Rotor Failure Scenario
3.1. Rotor Failure Criterion
3.2. Crack Propagation in the Turbine Rotor
4. Optimization of Diagnostic Testing Intervals
4.1. SPT-Based Estimation of the Decrease in Rotor Steel Toughness
4.2. Probability of the Turbine Rotor Failure
4.3. Optimization of Diagnostic Testing Intervals
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Maintenance Type | Description |
---|---|
Reactive | It is the most popular type of maintenance, commonly used for noncritical assets with low failure probability. It relies on repairing elements only after breakdown or poor performance [29,30]. |
Preventive | This type of maintenance is based on preventing failure through periodic inspection, diagnosis and observation. It is used for assets with a predictable failure frequency connected with wear processes. Its main purpose is to extend the lifetime of elements [29,31]. |
Predictive | This is a prediction-based maintenance type using algorithms and machine learning to provide the most reliable condition monitoring and failure alerting. For this method, it is necessary to carry out appropriate measurements [18,29]. |
Routine | It is strongly connected with time-based maintenance that relies on regular inspections and servicing. Actions lead to decrease of breakdown possibility owing to continuous care of the technical condition [14,32]. |
Prescriptive | It is the method complementary to the predictive one in which the diagnosis process and repair guidance is provided. It is also focused on failure impact and the action priority [29]. |
Opportunistic | In this method, downtime of the plant caused by element failure is the opportunity to take care of other areas, even when the breakdown is not found [31]. |
Condition-Based (CBM) | CBM requires real-time monitoring of a specific parameter to detect failure symptoms. Early detection of irregularities allows prevention from serious breakdown or reduction of the undesirable effects. This method requires data collection, effective results interpretation, decision making and intervention. In CBM, the most frequently used techniques are vibration analysis, infrared thermography, ultrasonic analysis and oil analysis [29,33]. |
Time-Based (TBM) | It is one of the most basic maintenance types. Element repair or replacement takes place at a specific interval without condition assessment. The method is popular for objects in which failure is cyclic and appears in fixed time. It is also used when diagnosis tests or measurements are not economically justified. The TBM is based on routine tasks often implemented by contracted services [34,35]. |
Cost-Optimal | This is the type of maintenance in which the main purpose is to minimize costs while ensuring the appropriate technical condition for assets. The main parameters that are taken into consideration during optimization process are cost of downtime due to a breakdown, cost of corrective maintenance (repair/replacement), cost of preventive maintenance, acceptable degree of degradation and inspection time [19]. |
Criticality-Based | In criticality-based maintenance, the most important aspects are the critical elements of the power plant. Main activities should focus on assets that have the largest effect on performance in the case of a breakdown [16]. |
Risk-Based (RBM) | RBM is the maintenance type which places the greatest emphasis on protecting assets with the greatest failure risk (failure probability times consequences). These assets are subjected to more frequent conservation and inspection. RBM is a preventive method of maintenance that allows for the reduction of costs and scope of activities [34,36]. |
Reliability Centered (RCM) | In the RCM method, three issues are taken into consideration: genesis of failure, its consequences and purpose of present prevention effort. The aim is to ensure high reliability of the system. It helps to find elements whose failure threaten further operation of the plant and which are not included in other maintenance types [18]. |
Failure Finding (FFM) | The aim of FFM is to find the hidden problems in elements which are not in constant use before the breakdown occurs. The searching process for latent failure is based on fixed time intervals or risk calculation [34] |
Input Data | Mean Value | Standard Deviation |
---|---|---|
200 MPa | 10 MPa | |
250 MPa | 12.5 MPa | |
300 MPa | 15 MPa | |
2 × 10−12 | 1 × 10−13 | |
3.4537 | 0.173 | |
65 MPa | 3.25 MPa | |
3 × 10−14 | 1.5 × 10−15 | |
5.6572 | 0.283 | |
1.98 | 0.086 | |
2–5 mm | 0.5 mm | |
100 MPa | 5 MPa |
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Tomala, M.; Rusin, A.; Wojaczek, A. Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility. Energies 2020, 13, 3464. https://doi.org/10.3390/en13133464
Tomala M, Rusin A, Wojaczek A. Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility. Energies. 2020; 13(13):3464. https://doi.org/10.3390/en13133464
Chicago/Turabian StyleTomala, Martyna, Andrzej Rusin, and Adam Wojaczek. 2020. "Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility" Energies 13, no. 13: 3464. https://doi.org/10.3390/en13133464