This paper is on the suggestion of maintenance items for electric railway facility systems. With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad electrical facility systems are selected for study. Design Structure Matrix (DSM) is utilized to establish considerations tailored to the maintenance characteristics, and the Fuzzy-TOPSIS methodology is utilized for determining the maintenance detail evaluation item baseline weights, a multi-criteria decision-making problem. Studies show that degradation, insulation items have the highest weight of 14.63%, and capacity items have the lowest weight of 5.34%. The results of this may be contributed to the underlying research in carrying out maintenance activities to ensure the reliability and safety of railroad electrical facility systems.
With the recent increase in the use of electric railroads, the utilization and importance of railroad electrical facility systems is also increasing. Railroad electrical systems are responsible for enabling power supply and substitution, signal control and communication, and at the subsystem level include subway power, signal control and communications. Currently, Korea's railroad electrical facility system is rapidly aging, and the Ministry of Land, Infrastructure and Transport announced the first basic plan for maintenance management of railroad facilities (2021−2025) as a countermeasure plan [
In line with the need for efficient maintenance of railroad electrical facility systems, underlying studies using various approaches have been conducted. Kang Hyun-il et al. conducted a maintenance optimization survey of railroad operators, conducting a research based on maintenance optimization [
Efficient and systematic standards are needed to perform efficient maintenance of aging railroad electrical facility systems, taking into account the limitations of economic and temporal costs, and the establishment of maintenance assessment criteria involves the decision maker's subjective judgment. Because decision-makers’ judgments are subjective and ambiguous, a methodology is needed to quantitatively derive opinions from experts in the field. Chen presents a methodology that extends the Technique for Order Performance by Ideal Solution (TOPSIS) logic in a fuzzy environment to solve the MCDM problem [
Here,
We obtain a regularization decision matrix V, weighted by a combination of the fuzzy matrix and the weighted matrix, and distance it from the FPRIP matrix A+ and FNRIP matrix A- for the alternative we want to select preferentially, using the maximum and minimum values for each matrix. The equation for the distance is expressed in
Next, we obtain the resulting values for CC, the relative proximity coefficient for each distance, and then, depending on the number of resulting values, we can derive the priorities of the alternatives to be chosen. The equation for obtaining CC is expressed in
Maintenance | Property |
---|---|
criteria | |
Safety | The performance to prevent casualties, damage and loss of rail facilities, under the requirements of rail facilities |
Durability | The performance of railroad facilities to maintain the required functions during the service life of railroad facilities |
Usability | The performance of providing adequate convenience and functionality in terms of the use and demand of rail facilities |
To consider the effectiveness of decisions in the selection of maintenance criteria, a process of grouping relatively similar items is needed. Therefore, the association of each criteria was expressed in Design Structure Matrix (DSM) and DSM Partitioning activities were performed to cluster similar items, reflecting the opinions of railroad electrical system maintenance experts. As a result, according to the evaluation criteria safety, durability and usability, there were 10 alternatives, and the results were
Linguistic variable | Fuzzy numbers |
---|---|
VL(Very Low) | (0.0, 0.0, 0.1, 0.2) |
L(Low) | (0.1, 0.2, 0.2, 0.3) |
ML(Medium Low) | (0.2, 0.3, 0.4, 0.5) |
M(Medium) | (0.4, 0.5, 0.5, 0.6) |
MH(Medium High) | (0.5, 0.6, 0.7, 0.8) |
H(High) | (0.7, 0.8, 0.8, 0.9) |
VH(Very High) | (0.8, 0.9, 1.0, 1.0) |
Linguistic variable | Fuzzy numbers |
---|---|
VP(Very Poor) | (0.0, 0.0, 0.1, 0.2) |
P(Poor) | (0.1, 0.2, 0.2, 0.3) |
MP(Medium Poor) | (0.2, 0.3, 0.4, 0.5) |
F(Fair) | (0.4, 0.5, 0.5, 0.6) |
MG(Medium Good) | (0.5, 0.6, 0.7, 0.8) |
G(Good) | (0.7, 0.8, 0.8, 0.9) |
VG(Very Good) | (0.8, 0.9, 1.0, 1.0) |
Criteria | Expert 1 | Expert 2 | Expert 3 |
---|---|---|---|
VH (0.80, 0.90, 1.00, 1.00) | H (0.70, 0.80, 0.80, 0.90) | VH (0.80, 0.90, 1.00, 1.00) | |
H (0.70, 0.80, 0.80, 0.90) | M (0.40, 0.50, 0.50, 0.60) | MH (0.50, 0.60, 0.70, 0.80) | |
MH (0.50, 0.60, 0.70, 0.80) | M (0.40, 0.50, 0.50, 0.60) | ML (0.20, 0.30, 0.40, 0.50) |
The decision matrices for weight selection of the evaluation criteria were Very Low (VL: 0.0, 0.0, 0.1, 0.2), Low (L: 0.1, 0.2, 0.2, 0.3), Medium Low (ML: 0.2, 0.3, 0.4, 0.5), Medium (M:0.4, 0.5, 0.5, 0.6), Medium High (MH: 0.5, 0.6, 0.7, 0.8), High (H: 0.7, 0.8, 0.8, 0.9), Very High (VH: 0.8, 0.9, 1.0, 1.0). The results are expressed in
Decision matrices for alternative weight selection were classified as Very Poor (VP: 0.0, 0.0, 0.1, 0.2), Poor (P: 0.1, 0.2, 0.2, 0.3), Medium Poor (MP: 0.2, 0.3, 0.4, 0.5), Fair (F:0.4, 0.5, 0.5, 0.6), Medium Good (MG: 0.5, 0.6, 0.7, 0.8), Good (G: 0.7, 0.8, 0.8, 0.9), Very Good (VG: 0.8, 0.9, 1.0, 1.0). The results are expressed in
Three railroad electrical system experts were cast to conduct a survey to select the weight of the evaluation criteria for railroad electrical system maintenance and a survey to select the weight of the maintenance criteria, respectively,
Criteria | Alternatives | Expert 1 | Expert 2 | Expert 3 |
---|---|---|---|---|
VG | G | VG | ||
MP | F | MP | ||
F | G | F | ||
MG | MG | MG | ||
F | P | MP | ||
G | P | F | ||
MG | MG | MP | ||
G | G | F | ||
MG | MP | MG | ||
P | P | P |
Criteria | Alternatives | Expert 1 | Expert 2 | Expert 3 |
---|---|---|---|---|
VG | VG | VG | ||
G | MP | P | ||
F | P | MP | ||
G | G | MP | ||
MP | G | MP | ||
G | P | F | ||
MG | G | MP | ||
MG | F | P | ||
G | MP | MP | ||
P | MP | MP |
Criteria | Alternatives | Expert 1 | Expert 2 | Expert 3 |
---|---|---|---|---|
VG | G | MG | ||
G | G | MP | ||
F | P | F | ||
G | F | P | ||
G | G | MP | ||
MG | F | F | ||
G | F | MG | ||
G | MG | P | ||
MP | MG | P | ||
MP | F | P |
Alternative | Criterion 1 | Criterion 2 | Criterion 3 |
---|---|---|---|
(0.70, 0.87, 0.93, 1.00) | (0.80, 0.90, 1.00, 1.00) | (0.50, 0.77, 0.83, 1.00) | |
(0.20, 0.37, 0.43, 0.60) | (0.10, 0.43, 0.47, 0.90) | (0.20, 0.63, 0.67, 0.90) | |
(0.40, 0.60, 0.60, 0.90) | (0.10, 0.33, 0.37, 0.60) | (0.10, 0.40, 0.40, 0.60) | |
(0.50, 0.60, 0.70, 0.80) | (0.20, 0.63, 0.67, 0.90) | (0.10, 0.50, 0.50, 0.90) | |
(0.10, 0.33, 0.37, 0.60) | (0.20, 0.47, 0.53, 0.90) | (0.20, 0.63, 0.67, 0.90) | |
(0.10, 0.50, 0.50, 0.90) | (0.10, 0.50, 0.50, 0.90) | (0.40, 0.53, 0.57, 0.80) | |
(0.20, 0.50, 0.60, 0.80) | (0.20, 0.57, 0.63, 0.90) | (0.40, 0.63, 0.67, 0.90) | |
(0.40, 0.70, 0.70, 0.90) | (0.10, 0.43, 0.47, 0.80) | (0.10, 0.53, 0.57, 0.90) | |
(0.20, 0.50, 0.60, 0.80) | (0.20, 0.47, 0.53, 0.90) | (0.10, 0.37, 0.43, 0.80) | |
(0.10, 0.20, 0.20, 0.30) | (0.10, 0.27, 0.33, 0.50) | (0.10, 0.33, 0.37, 0.60) | |
Weight | (0.70, 0.87, 0.93, 1.00) | (0.40, 0.63, 0.67, 0.90) | (0.20, 0.47, 0.53, 0.80) |
Alternative | Criterion 1 | Criterion 2 | Criterion 3 |
---|---|---|---|
(0.49, 0.75, 0.87, 1.00) | (0.32, 0.57, 0.67, 0.90) | (0.10, 0.36, 0.44, 0.80) | |
(0.14, 0.32, 0.40, 0.60) | (0.04, 0.27, 0.31, 0.81) | (0.04, 0.30, 0.36, 0.72) | |
(0.28, 0.52, 0.56, 0.90) | (0.04, 0.21, 0.24, 0.54) | (0.02, 0.19, 0.21, 0.48) | |
(0.35, 0.52, 0.65, 0.80) | (0.08, 0.40, 0.44, 0.81) | (0.02, 0.23, 0.27, 0.72) | |
(0.07, 0.29, 0.34, 0.60) | (0.08, 0.30, 0.36, 0.81) | (0.04, 0.30, 0.36, 0.72) | |
(0.07, 0.43, 0.47, 0.90) | (0.04, 0.32, 0.33, 0.81) | (0.08, 0.25, 0.30, 0.64) | |
(0.14, 0.43, 0.56, 0.80) | (0.08, 0.36, 0.42, 0.81) | (0.08, 0.30, 0.36, 0.72) | |
(0.28, 0.61, 0.65, 0.90) | (0.04, 0.27, 0.31, 0.72) | (0.02, 0.25, 0.30, 0.72) | |
(0.14, 0.43, 0.56, 0.80) | (0.08, 0.30, 0.36, 0.81) | (0.02, 0.17, 0.23, 0.64) | |
(0.07, 0.17, 0.19, 0.30) | (0.04, 0.17, 0.22, 0.45) | (0.02, 0.16, 0.20, 0.48) |
Based on the results for
Criteria | Criterion 1 | Criterion 2 | Criterion 3 |
---|---|---|---|
(1.00, 1.00, 1.00, 1.00) | (0.90, 0.90, 0.90, 0.90) | (0.80, 0.80, 0.80, 0.80) | |
(0.07, 0.07, 0.07, 0.07) | (0.04, 0.04, 0.04, 0.04) | (0.02, 0.02, 0.02, 0.02) |
Find the displacement between FPIRP and FNIRP determined for each evaluation criteria from the fuzzy data presented in
FPIRP | Criterion 1 | Criterion 2 | Criterion 3 |
---|---|---|---|
d( |
0.291 | 0.353 | 0.451 |
d( |
0.656 | 0.609 | 0.509 |
d( |
0.488 | 0.666 | 0.598 |
d( |
0.451 | 0.533 | 0.552 |
d( |
0.701 | 0.579 | 0.509 |
d( |
0.608 | 0.594 | 0.523 |
d( |
0.569 | 0.548 | 0.494 |
d( |
0.448 | 0.614 | 0.540 |
d( |
0.569 | 0.579 | 0.582 |
d( |
0.822 | 0.696 | 0.611 |
d( |
0.733 | 0.611 | 0.477 |
d( |
0.339 | 0.425 | 0.412 |
d( |
0.542 | 0.283 | 0.263 |
d( |
0.537 | 0.471 | 0.386 |
d( |
0.317 | 0.436 | 0.412 |
d( |
0.495 | 0.435 | 0.361 |
d( |
0.477 | 0.459 | 0.413 |
d( |
0.583 | 0.384 | 0.394 |
d( |
0.477 | 0.436 | 0.336 |
d( |
0.139 | 0.233 | 0.255 |
Alternative | CC | Weight [%] | ||
---|---|---|---|---|
1.095 | 1.820 | 0.624 | 14.63 | |
1.774 | 1.175 | 0.399 | 9.34 | |
1.752 | 1.088 | 0.383 | 8.98 | |
1.536 | 1.395 | 0.476 | 11.15 | |
1.789 | 1.165 | 0.394 | 9.25 | |
1.725 | 1.290 | 0.428 | 10.03 | |
1.611 | 1.349 | 0.456 | 10.68 | |
1.603 | 1.362 | 0.459 | 10.77 | |
1.730 | 1.249 | 0.419 | 9.83 | |
2.128 | 0.628 | 0.228 | 5.34 |
This paper conducted a weight determination study on the maintenance detail criteria items with the aim of ensuring reliability and safety of railroad electrical facility systems in South Korea. safety, durability, and usability were selected as evaluation criteria, and the detailed criteria for maintenance of railroad electrical facility systems were divided into degradation, insulation, abrasion, strength, noise, corrosion, crack, oil leak, slope, subsidence, elapsed years, number of uses, environment, number of services, number of failure detection, product discontinued, number of maintenance, capacity. An alternative to the evaluation criteria was described as Design Structure Matrix, reflecting the opinions of railroad electrical system maintenance experts, and clustering of alternatives was performed through partitioning. As a result, it could be reduced to 10 alternatives with similar evaluation properties. To determine the weighting of the maintenance detail basis alternatives by the evaluation criteria, the weighting of the assessment criteria and the weighting of the basis alternatives were represented in fuzzy matrix, and the CC values were calculated by determining FPIRP and FNIRP. By normalizing the results for this, the weights of alternatives for each railroad electrical facility maintenance detail criteria item are presented as Percentage. As a result, degradation, insulation items were weighted the highest at 14.63%, and capacity items were weighted the lowest at 5.34%. A final comparison of this study's conclusions through peer review resulted in a valid opinion. The results of this study may contribute to the creation of a maintenance manual for electrical railway facilities systems to be carried out in the future. Also, the results of this may be contributed to the underlying research in carrying out maintenance activities to ensure the reliability and safety of railroad electrical facility systems. The limitation of this paper is how to select the scope of experts in electric railroad facility systems. Further research is also needed to determine the range of membership functions and survey scales for obtaining the Fuzzy matrix. Future research will change the process of drawing conclusions respectively. In addition, each conclusion will be compared and peer reviewed to carry out results verification and to produce reasonable procedures.