This research adopted a mixture of research techniques (qualitative and quantitative) to ensure the development of a functional maintenance strategy framework for electric power plants (effective and ineffective impacts). The maintenance strategy was examined using critical parameters, including preventive maintenance (PM), predictive maintenance (PdM), and corrective maintenance (CM) strategies. Each strategy has its own effective and ineffective impacts, as presented below.
3.3. Ineffective Impacts of Maintenance Strategies
A poorly planned or executed maintenance strategy has adverse impacts on electric power plants. These include the following:
- i.
Inadequate maintenance practices lead to more frequent breakdowns and failures, leading to increased downtime. This practice disrupts the power generation process, reduces reliability, and inconveniences consumers who rely on a consistent power supply;
- ii.
Neglecting maintenance can lead to minor issues escalating into major failures. This can lead to costly repairs or equipment replacements. Reactive maintenance that involves fixing power plant issues only after they occur often results in higher repair expenses as compared to proactive maintenance strategies;
- iii.
Insufficient maintenance highly compromises the safety of plant personnel and the surrounding environment. Therefore, ageing equipment, electrical faults, and malfunctioning systems increase the likelihood of accidents, electrical hazards, and environmental incidents.
- iv.
Without regular maintenance, power plant equipment will experience reduced efficiency due to wear and tear, dirt accumulation, or inefficient operational procedures. These issues result in higher energy consumption, increased emissions, and decreased overall plant performance.
- v.
Power plants can have various regulations and environmental standards. Inadequate maintenance practices can lead to non-compliance, potentially resulting in penalties, legal issues, and damage to the plant’s reputation.
According to this research, the major difference between the effective and ineffective impacts of maintenance strategies is their clear distinction in terms of efficiency. Effective impacts provide the best practices for ease of maintenance, and ineffective impacts have several inadequacies.
In terms of simulation, the use of a randomised Markov chain model was adopted to examine the demographic data (see
Table 2) and present the results in graphical form. A hypothetical approach was used in this research to analyse the methodology of using the effective and ineffective impacts of an electric power plant’s maintenance strategy. The research method followed the rules, procedures, and methods that were used to collect and analyse data. Also, the data generated were efficiently used to draw conclusions from the results of this study.
Demographic analysis was conducted to determine and interpret the data in terms of gender, age group, educational level, current designation, and work experience using frequencies and percentages.
Table 2 below presents the demographic analysis.
In addition,
Table 2 can be graphically illustrated, as shown in
Figure 1 below.
Table 2.
Demographic analysis.
Table 2.
Demographic analysis.
Gender | Frequency | Percentage |
---|
Female | 23 | 30 |
Male | 54 | 70 |
Total | 77 | 100 |
Age group | | |
25–30 | 16 | 21 |
31–35 | 35 | 45 |
36–45 | 22 | 29 |
45 Above | 4 | 5 |
Total | 77 | 100 |
Current Designation | | |
Maintenance manager | 16 | 21 |
Operational manager | 25 | 32 |
Owner | 17 | 22 |
Plant manager | 19 | 25 |
Total | 77 | 100 |
Work experience | | |
0–5 | 38 | 49 |
6–10 | 26 | 34 |
11–15 | 0 | 0 |
16–20 | 5 | 7 |
More than 20 years | 8 | 10 |
Total | 77 | 100 |
To further analyse the hypotheses,
Table 3 below presents three hypotheses based on Pearson chi-square tests, likelihood ratios, and N of valid cases with respect to their value, df, and asymptotic significance.
Hypothesis 1. states that there is a significant relationship between installed capacity and the fact that maintenance of a plant is crucial for the safety and preservation of the longevity of plant assets.
Hypothesis 2. states that there is a significant relationship between the size of a plant and the fact that equipment and machinery deteriorate if proper plant maintenance practices are not followed. The null hypothesis states that there is not a significant relationship.
Hypothesis 3. states that there is a significant relationship between accidents and the fact that people who work in poorly maintained plants run the danger of getting hurt. The null hypothesis states that there is not a significant relationship.
Table 3.
Chi-square test for Hypotheses 1–3.
Table 3.
Chi-square test for Hypotheses 1–3.
Chi-Square Test H1 | Chi-Square Test H2 | Chi-Square Test H3 |
---|
| Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) |
---|
Pearson Chi-square | 26.34a | 16 | 0.047 | 13.690a | 6 | 0.33 | 31.469a | 16 | 0.12 |
Likelihood ratio | 30.061 | 16 | 0.018 | 14.085 | 6 | 0.029 | 29.352 | 16 | 0.022 |
N of valid Cases | 77 | | | 77 | | | 77 | | |
Several variables were considered in this analysis. However, the maintenance capacity has an insignificant output in electric power plants as it is crucial for the safety and preservation of the longevity of plant assets. From
Table 3 above, we can see that the significance value of the Pearson chi-square test was 0.047, which is less than the
p-value of 0.05. Hence, we reject the null hypothesis and conclude that there is a significant relationship between installed capacity and the fact that the maintenance of a plant is crucial for the safety and preservation of the longevity of plant assets. This is applicable to Hypotheses 2 and 3. Hypotheses 4–6 are presented (as in
Table 4) below.
Hypothesis 4. states that there is a significant relationship between the problems of shutdown and the fact that equipment and machinery will deteriorate if proper plant maintenance practices are not followed. The null hypothesis states that there is not a significant relationship.
Hypothesis 5. states that there is a significant relationship between equipment failure and the fact that maintenance of a plant is crucial for the safety and preservation of the longevity of plant assets. The null hypothesis states that there is not a significant relationship.
Hypothesis 6. states that there is a significant relationship between regular and scheduled maintenance to minimise risks to employees and the fact that people who work in poorly maintained plants run the danger of getting hurt. The null hypothesis states that there is not a significant relationship.
We considered the following variables: How often do you come across problems of shutdown due to improper maintenance of equipment at your plant? Do you believe that equipment and machinery will deteriorate if proper plant maintenance practices are not followed?
Table 4.
Chi-square test for Hypotheses 4–6.
Table 4.
Chi-square test for Hypotheses 4–6.
Chi-Square Test H4 | Chi-Square Test H5 | Chi-Square Test H6 |
---|
| Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) |
---|
Pearson Chi-square | 27.262a | 12 | 0.007 | 27.262a | 12 | 0.007 | 33.582a | 16 | 0.006 |
Likelihood ratio | 29.262 | 12 | 0.004 | 29.262 | 12 | 0.004 | 31.561 | 16 | 0.011 |
N of valid Cases | 77 | | | 77 | | | 77 | | |
From
Table 4 above, we can see that the significance value of the Pearson chi-square test was 0.007, which is less than the
p-value of 0.05. Hence, we reject the null hypothesis and conclude that there is a significant relationship between the problems of shutdown and the fact that equipment and machinery will deteriorate if proper plant maintenance practices are not followed. This is applicable to Hypotheses 5 and 6. Hypotheses 7–9 are presented (as in
Table 5) below.
Hypothesis 7. states that there is a significant relationship between maintenance strategy practices and the formulation of a maintenance strategy. The null hypothesis states that there is not a significant relationship.
Hypothesis 8. states that there is a significant relationship between appropriate manpower management and factors considered while enhancing the maintenance of a plant. The null hypothesis states that there is not a significant relationship.
Hypothesis 9. states that there is a significant relationship between the condition or nature of a plant and factors considered while enhancing the maintenance of a plant. The null hypothesis states that there is not a significant relationship.
Table 5.
Chi-square test for Hypotheses 7–9.
Table 5.
Chi-square test for Hypotheses 7–9.
Chi-Square Test H7 | Chi-Square Test H8 | Chi-Square Test H9 |
---|
| Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) |
---|
Pearson Chi-square | 26.490a | 16 | 0.048 | 25.376a | 12 | 0.013 | 31.037a | 16 | 0.013 |
Likelihood ratio | 29.011 | 16 | 0.024 | 26.474 | 12 | 0.009 | 36.962 | 16 | 0.002 |
N of valid Cases | 77 | | | 77 | | | 77 | | |
We considered the following variables: Is it possible for maintenance strategy practices to be conducted in the electric power plant? And how is the maintenance strategy formulated?
From
Table 5 above, we can see that the significance value of the Pearson chi-square test was 0.048, which is less than the
p-value of 0.05. Hence, we reject the null hypothesis and conclude that there is a significant relationship between maintenance strategy practices and the formulation of a maintenance strategy. This is applicable to Hypotheses 8 and 9.
Hypotheses 10–13 are presented (as in
Table 6) below.
Hypothesis 10. states that there is a significant relationship between the age of a plant and the maintenance strategy. The null hypothesis states that there is not a significant relationship.
Hypothesis 11. states that there is a significant relationship between the safety measures deployed and the factors considered while enhancing the maintenance of a plant. The null hypothesis states that there is not a significant relationship.
Hypothesis 12. states that there is a significant relationship between not adopting maintenance and formulating a maintenance strategy. The null hypothesis states that there is not a significant relationship.
Hypothesis 13. states that there is a significant relationship between companies interested in the maintenance of their plant and factors considered while enhancing the maintenance of the plant. The null hypothesis states that there is not a significant relationship.
Table 6.
Chi-square test for Hypotheses 10–13.
Table 6.
Chi-square test for Hypotheses 10–13.
Chi-Square Test H10 | Chi-Square Test H11 | Chi-Square Test H12 | Chi-Square Test H13 |
---|
| Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) | Value | df | Asymptotic Significance (2-Sided) |
---|
Pearson chi-square | 20.348a | 12 | 0.61 | 18.439a | 16 | 0.299 | 39.088a | 16 | 0.001 | 23.591a | 12 | 0.023 |
Likelihood ratio | 21.011 | 12 | 0.050 | 21.910 | 16 | 0.146 | 46.134 | 16 | 0.000 | 24.523 | 12 | 0.017 |
| 77 | | | 77 | | | 77 | | | 77 | | |
We considered the following variables: How old is your power plant? And how do you formulate your plant’s maintenance strategy?
From
Table 6 above, we can see that the significance value of the Pearson chi-square square test was 0.061, which is greater than the
p-value of 0.05. Hence, we do not reject the null hypothesis and conclude that there is no significant relationship between the age of a plant and the maintenance strategy. This is applicable to Hypotheses 10–13.