Impact analysis of field maintenance practices on reliability metrics

2.1.1. Thermodynamic characteristics

Fig. 2(a) illustrates the thermodynamics diagram of a GE MS 5001 gas turbine, which is designed based on the Brayton cycle. This diagram showcases the relationship between pressure and volume throughout the four phases of the gas turbine cycle. The compression phase (1-2) involves compressing air in the compressor, which increases its pressure and decreases its volume. Following this, the combustion phase (2-3) takes place. In this phase, the compressed air is mixed with fuel and undergoes combustion at constant pressure, releasing significant energy. The expansion phase (3-4) occurs in the turbine, where the high-pressure, high-temperature gases expand and perform mechanical work. This work reduces both the pressure and temperature of the gases, ultimately generating electrical power. Finally, the exhaust phase (4-1) involves discharging the exhaust gases, completing the cycle. The Temperature-Entropy (TS) diagram provides a more detailed view of the thermodynamic changes that occur within the gas turbine cycle. It shows the isentropic compression (1-2 s) and expansion (3-4 s) phases as vertical segments, indicating no change in entropy. However, the real compression (1-2) and expansion (3-4) stages deviate from these ideal lines due to friction and heat transfer. The combustion phase (2-3) occurs at constant entropy, meaning there is no change in entropy during this period. Together, these diagrams illustrate the key processes and thermodynamic changes that take place in the gas turbine cycle.

Fig. 2Gas turbine cycles example

a) Brayton cycle [16]

b) Single cycle, single shaft gas turbine [16]

Fig. 2(b) shows a single-shaft, simple-cycle gas turbine schematic. The diagram shows the steps involved in the process. Under standard ISO conditions (59°F/15°C, 14.7 psi/1.013 bar, 60 % relative humidity), air enters the axial flow compressor at point 1. At this stage, the air is compressed, increasing pressure and temperature. The compressed air then exits the compressor at point 2 and enters the combustion system. In this system, fuel is injected and combusted at a constant pressure. The purpose of the combustion system is to ensure thorough mixing, burning, dilution, and cooling. As a result, the gases exiting at point 3 have an average temperature. These hot gases then proceed to the turbine section. In the turbine section, the energy of the gases is converted into work through two stages: the nozzle section, which expands the gases into kinetic energy, and the bucket section, which converts this kinetic energy into mechanical work. The turbine powers the generator and provides energy at the gas turbine's output flange. Typically, more than half of the turbine's work is used to drive the generator.

2.1.2. Types of inspections

The GE MS5001 gas turbine necessitates various critical inspections crucial for ensuring reliable operation and prolonging its service life. Each inspection type plays a specific role in the comprehensive maintenance schedule of the GE MS5001 gas turbine. BI offers routine, non-invasive assessments to detect early signs of wear. These inspections form part of a proactive maintenance strategy aimed at evaluating diverse turbine components and identifying any potential issues at an early stage. Key inspection types encompass BI, CI, HGPI, and MI.

Fig. 3Types of inspections

a) BI pots [17]

b) CI area [17]

c) HGPI area [17]

d) MI area [17]

Boroscope Inspection, depicted in Fig. 3(a), is a non-invasive visual inspection method that involves using a boroscope, a fiber-optic instrument, to examine the internal components of a gas turbine without the need for disassembly. This type of inspection allows maintenance teams to visually assess the condition of critical parts such as blades, vanes, and combustion chambers. It is typically carried out during short operational outages or planned maintenance intervals to detect issues like erosion, cracks, or foreign object damage early on, thereby reducing downtime and preventing major failures.

Combustion inspection, as illustrated in Fig. 3(c), focuses specifically on the combustion section of the gas turbine, including components like combustion liners, transition pieces, and fuel nozzles. This inspection is usually conducted after a certain number of operating hours or starts to evaluate the wear and tear on components exposed to high temperatures and thermal cycling. Regular combustion inspections help prevent combustion-related failures that could lead to unplanned outages or decreased efficiency by replacing or repairing worn parts.

Hot gas path inspection, as shown in Fig. 3(b), is a more detailed examination that concentrates on the high-temperature sections of the gas turbine, such as turbine nozzles, blades, and shrouds. This inspection involves partial disassembly of the turbine to thoroughly examine parts most susceptible to heat-related degradation. It is performed after longer periods of operation or according to OEM recommendations to identify issues like thermal fatigue, cracking, or deformation, ensuring damaged parts are replaced promptly to prevent significant damage or performance loss.

MI, as depicted in Fig. 3(d), is the most comprehensive type of inspection conducted on a gas turbine, involving a complete teardown of the turbine to thoroughly examine all major components, including the compressor, combustor, and turbine sections. MI are typically scheduled after a significant number of operating hours (or years) to restore the turbine to like-new condition. During this inspection, critical parts are carefully inspected, and any worn or damaged components are replaced or refurbished [18]. A Major Inspection helps extend the overall life of the turbine, ensuring long-term reliability and efficiency. Though it is time-consuming and costly, it is essential for maintaining the operational integrity of the gas turbine.

2.2.1. Mean time to repair (MTTR)

The MTTR is the average amount of time required to fix or restore a gas turbine to normal operation after a malfunction or failure. This accounts for the time required to locate the problem, replace the problematic parts, make the necessary repairs, and conduct the necessary testing and inspections before resuming operation [22]. The MTTR is a crucial metric that operators and maintenance personnel should monitor to ensure the overall reliability and availability of gas turbine units, it is expressed thus:

Where $DT$ is the downtime hours and $F$ is the number of failures. The objective is to minimize the MTTR while simultaneously increasing the power plant's output and profitability. A lower MTTR indicates that the plant has a quick rate of recovery and is well-maintained. Depending on the type and condition of the equipment, world-class MTTR varies. Accurately quantifying MTTR requires the use of CMMS or ERP with suitable processes and timestamps that collect data from multiple sources.

2.2.2. Mean time between failures (MTBF)

The MTBF of a gas turbine is the average time interval between two consecutive turbine failures or the expected operating time you can expect from the turbine before it breaks down [23]. A higher MTBF suggests that the plant is well-maintained and reliable. A gas turbine's MTBF can be influenced by a variety of factors, including the turbine's design, component quality, operating conditions, maintenance procedures, and other external factors. GTs have MTBF ratings that normally range from 20,000 to 50,000 hours or more, meaning that they are built to last for many thousands of hours before failing. However, with different turbines and their operating circumstances, the actual MTBF value can differ significantly. It's crucial to remember that MTBF is only a statistical estimate and cannot ensure that a gas turbine will function without failure for a predetermined period. It is only an estimate derived from past performance and additional variables. For this reason, it’s crucial to conduct routine maintenance and inspections on gas turbines to guarantee their reliability and safe functioning. MTBF is expressed as:

where TH is total hours, UT is uptime hours. When calculating a GT's MTBF, historical data on the failures of similar gas turbines can be used. Specifically, as shown in Eq. (2), the MTBF of a power station is determined by dividing the total operating time of all the gas turbines in the dataset by the number of failures that occurred during that time (in this study, 8760 yearly hours are considered). World-class MTBF varies and depends on equipment type, age, and duty cycle.

2.2.3. Availability (A)

The percentage of time that the GT is usable is indicated by this. An operational and well-maintained plant has a high availability rate; on the other hand, a low availability rate indicates problems that need to be fixed. The percentage of a certain period, usually a year that a GT is available and operational is referred to as its availability. Availability is a key performance indicator for evaluating the efficacy of operating and maintenance strategy, it is a measure of a GT's reliability and performance. Availability and reliability are two distinct metrics used to assess the performance of a system or piece of equipment, one could think of reliability as a subset of availability. The ratio of the GT's entire operating time to the total amount of time it was planned to be available for operation is commonly used to determine GT availability. The following equations, Eqs. (3-5), is usually used to determine a GT’s availability: where TH is total hours, which is equal to 8760 hours, Rh is run hours:

The formula for availability in terms of MTBF and MTTR is given by:

Relatively, for average Availability ($A_{ave}$) of several years, $y_1$, $y_2$, $y_3$, $y_4$, $y_5$, $y_6$,…, $y_n$:

A highly available machine may not necessarily be reliable if it frequently experiences downtime or failures. Reliability assesses the impact of lost time on operations, focusing on the likelihood of failure and the consequences of those failures. In contrast, availability measures the quantity of time an equipment is operational and accessible for use. An equipment can have high availability by being operational for long periods but may still lack reliability if it is prone to frequent breakdowns. Businesses need to consider both reliability and availability metrics to ensure optimal performance and minimize disruptions in operations. Balancing these factors can lead to a more robust and efficient equipment management strategy.

2.2.4. Reliability (R)

The ability of a GT to perform as intended under particular conditions without malfunctioning is known as its reliability. Gas turbine power plants' output, efficiency, long-term sustainability, safety, and environmental performance are all significantly impacted by reliability [24]. It is typically expressed as an approximate probability of success over a specified period. Well-maintained GTs are thought to be reliable and able to run continuously for extended periods. The amount of time that equipment functions without breaking down is usually used to measure its reliability. For equipment that has inbuilt redundancy, it is necessary to account for all possible outcomes or modes of redundancy when estimating the probability of failure. If a piece of equipment is meant to function for 5,000 hours without experiencing any problems, for example, and it does so, then it can be said to be 100 % reliable; if not, then it is not. The probability that the equipment will stay operational beyond a given time t is represented by the reliability function, symbolized as (𝑡). The reliability function can be written mathematically as follows:

The equipment failure probability is calculated using the natural logarithm in Eq. (6), where $t$ is the time, $\lambda$ is the failure rate per unit time, and $R\left(t\right)$ is the reliability function at time $t$. This formula indicates that the probability of failure is independent of the equipment's lifespan because it assumes that the failure rate will remain constant over time. Reliability function in reliability engineering is the chance that a system will function without failure for a given amount of time. The formula demonstrates how a system's reliability decreases exponentially over time as failures occur. In contrast, a lower failure rate denotes high reliability, whereas a high failure rate suggests a low gas turbine power plant reliability over time. In real-world situations, this formula could not always hold because it presupposes that the failure rate, $\lambda$, remains constant throughout time. The relationship between reliability and failure rate is reflected in the exponential decay function $R\left(t\right)$, which indicates that reliability declines with time as failures happen.

Availability, however, can have an impact on reliability. A system that is available 70 % of the time, for example, is most likely reliable 70 % of the time (a 100 % probability is assumed in this case) or at 100 % gas turbine power plant utilization. Reliability, however, can also consider additional factors like the possibility of a malfunction while the system is in operation, maintenance downtime, etc. Therefore, reliability emphasizes the likelihood of a system operating successfully over time, whereas availability tells you how frequently a system is operational. Based on this supposition, the relationship between availability and reliability is expressed as:

Boosting reliability can be achieved in many ways, including lowering a system’s complexity, increasing the reliability of its parts, using high-durability (design for reliability) components, utilizing standby and parallel redundancies, testing, and validation [27]. Establishing stringent quality control measures during the production or manufacturing process can help identify defects and deviations from specifications before they impact the finished product. Similarly, a well-defined maintenance plan that includes inspections, repairs, and preventive maintenance can help identify and address issues before they become failures.

2.2.5. Failure rate ($\mathit{\lambda }$)

The failure rate of an asset is the frequency with which it performs below expected levels or is not used to its full potential. It is frequently expressed as the number of failures per unit of time or use and is a helpful metric for assessing the reliability and effectiveness of a Gas Turbine Plant. A low failure rate indicates improved quality and reliability, whereas a high failure rate suggests subpar quality, flaws in the design, or insufficient maintenance. The failure rate is expressed as:

Equipment failures can be inadvertently caused by errors made by operators, maintenance personnel, or other staff members. A high failure rate is indicative of low system reliability. Premature equipment failures can also be brought on by poor maintenance practices, and insufficient or inconsistent maintenance deployment, such as neglecting routine maintenance or failing to follow advised maintenance practices. Equipment components may eventually deteriorate and become worn out as a result of age or repeated stress; if the primary equipment fails, vital components lacking backup or redundant systems may fail the entire system.

Analysing failure rates is essential for various aspects such as reliability prediction, maintenance planning, risk management, and cost management [26]. It serves multiple purposes, including predicting the reliability and lifespan of systems and components, informing maintenance schedules and preventive maintenance strategies, identifying critical components with high failure rates, and reducing maintenance and operational costs. This analysis allows for the prevention of unexpected failures and the optimization of spare parts inventory.

2.2.6. Downtime hours (DT)

Equipment downtime is the period that a machine or piece of equipment is not operating as intended or is not usable for a variety of reasons, including maintenance, repairs, malfunctions, or other problems. Equipment outages can be classified into three categories: scheduled, unscheduled, and unanticipated. Unplanned downtime happens suddenly and unexpectedly, whereas planned downtime is scheduled and planned. Unplanned downtime is not unexpected, and it is also not prearranged. Many variables can be blamed for equipment downtime, including maintenance and repair, human factors, equipment failure, supply chain problems, environmental conditions, age, and obsolescence. Equipment downtime can result in lower output, higher expenses, and problems with quality control, inventory accumulation, and reputational damage. Strategies including routine maintenance, predictive maintenance, equipment upgrades, operator training, inventory and spares management, quality control procedures, and emergency response planning can all be used to minimize equipment downtime. Manufacturing organizations can boost performance, optimize cost, and increase production by comprehending the reasons behind equipment downtime and putting efficient plans in place to cut down on downtime hours [27]. Equipment yearly downtime hours are mathematically expressed as:

Tracking total downtime hours, MTBF, MTTR, and percentage downtime are critical aspects of measuring equipment downtime. Total downtime hours provide a straightforward measure of the cumulative time an equipment is out of operation.

3.6.1. Reliability versus CI = BI

Fig. 4 shows a chart comparing reliability values and CIs. It demonstrates the impact of the number of combustion inspections conducted on five gas turbine units (2540A to 2540E) and their respective reliability percentages. While it is advised that all units undergo 13 inspections, the actual inspection counts vary, influencing overall reliability outcomes. The bars and line projections illustrate the variances.

Fig. 4Combustion inspection adherence

Only 2 CI inspections were conducted in Unit 2540A, well below the recommended amount, resulting in a low reliability of 65 %. This substantial deviation from the recommended inspections had a negative effect on the unit's performance. Unit 2540B completed 6 inspections, leading to a higher reliability of 86 %, showcasing that even partial compliance can maintain relatively good reliability. Unit 2540C, with 4 inspections, displayed a reliability of 68 %, indicating that fewer inspections can result in decreased reliability. Units 2540D and 2540E, both with 7 inspections, achieved the highest reliability of 94 %, emphasizing that exceeding half of the recommended inspections significantly boosts performance.

Considering these findings, several recommendations can be proposed. Units with lower reliability, such as 2540A and 2540C, should increase their inspection frequency to at least 7 or more, to enhance their reliability. The current inadequate inspection numbers likely contribute to operational inefficiencies and increased failure rates. Encouraging full compliance with the recommended 13 inspections is advisable, as units closer to this target, like 2540D and 2540E, demonstrated notably higher reliability. Furthermore, implementing predictive maintenance could optimize inspection schedules by prioritizing units needing attention, ensuring greater reliability without strictly adhering to OEM guidelines. In summary, elevating inspection frequency, especially for underperforming units, and striving for closer adherence to the recommended schedule will elevate reliability levels and decrease the likelihood of failures.

3.6.2. Reliability versus HGPI

Fig. 5 displays a chart that illustrates the relationship between the number of hot HGPI recommended and the number conducted for the five gas turbine units (2540A to 2540E), and how this adherence to inspection schedules influences the reliability of each unit. Across all units, as shown in the bar heights, the recommended number of hot gas path inspections remains constant at 4, represented by the gray bars in the chart. Discrepancies arise in the actual number of inspections performed, depicted by the light blue bars, leading to varying levels of reliability among the units. Unit 2540A only completed 1 out of the prescribed 4 inspections, resulting in a reliability percentage of 65 %. Similarly, two inspections were conducted in unit 2540C out of the recommended 4, yielding a reliability rating of 68 %. In contrast, units 2540B, 2540D, and 2540E adhered more closely to the recommended inspection frequency, completing 3 out of 4 inspections. This higher compliance correlates with improved reliability scores, with 2540B achieving 86 % and both 2540D and 2540E reaching 94 % reliability.

Fig. 5Hot gas path inspection adherence

The data underscores a clear association: a higher frequency of HGPI corresponds with heightened reliability. Units that closely follow the recommended inspection schedule demonstrate improved operational reliability. To enhance the reliability of the gas turbine units, it is essential to ensure alignment between the actual number of inspections and the recommended frequency. Units such as 2540A and 2540C, which exhibit lower reliability due to fewer inspections, would likely benefit from completing the recommended 4 inspections to enhance performance and prolong operational lifespan.

Addressing the disparity between recommended and actual inspections calls for implementing a robust inspection scheduling system to ensure compliance with the required number of inspections within specified intervals. Monitoring and evaluating maintenance practices can help identify and rectify any inefficiencies in inspection processes, ultimately improving compliance rates. Adopting predictive maintenance strategies that leverage real-time monitoring and data analysis can aid in anticipating when inspections are due based on equipment conditions. This approach reduces the risk of missed inspections and contributes to enhancing equipment reliability, thereby minimizing downtime and boosting operational efficiency. In conclusion, prioritizing compliance with recommended inspection frequencies is vital for maintaining high reliability across gas turbine units. By implementing improved scheduling, monitoring practices, and predictive maintenance measures, operational performance can be optimized to mitigate downtime risks and enhance overall efficiency.

3.6.3. Reliability versus MI

Fig. 6 displays the data and chart on MI carried out within the study evaluation period, it reveals a significant discrepancy between the recommended and actual number of inspections carried out for GTUs 2540A to 2540E. For units 2540A, 2540B, and 2540C, no MI was performed despite the recommendation for two inspections. Meanwhile, units 2540D and 2540E had only one inspection completed out of the recommended two. These scenarios are illustrated with the grey and blue bars as well as the line projections in the chart. This underperformance in inspection activities raises concerns about equipment reliability and the effectiveness of maintenance management processes.

Fig. 6Major inspection adherence

The failure to conduct the necessary inspections poses potential risks to equipment reliability and operational safety. Inspections play a critical role in identifying early signs of wear, damage, or possible failures. Without timely inspections, there is a higher probability of unexpected breakdowns, which could lead to costly downtime and safety hazards. The gap between recommended and actual inspections increases the likelihood of equipment failures, directly impacting operational reliability. Moreover, this discrepancy points to potential inefficiencies in the maintenance scheduling or resource allocation processes. It suggests possible challenges such as insufficient manpower, conflicting maintenance priorities, or a lack of emphasis on proactive maintenance strategies. Addressing these issues is essential to improving maintenance effectiveness and ensuring that equipment operates reliably. To mitigate these risks, it is recommended that the maintenance scheduling and resource allocation strategy be revisited. First, the inspection frequency should be re-evaluated to ensure it aligns with the criticality of the equipment and operational needs. This would help mitigate potential risks associated with under-maintained equipment. Next, measures should be implemented to improve monitoring and compliance with inspection schedules. This may include the use of enhanced tracking systems or introducing incentives to encourage adherence to the schedule. Lastly, optimizing maintenance planning processes will be crucial in prioritizing critical inspections and ensuring resources are allocated more effectively.