Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive Maintenance Article Swipe
Masoumeh Khoshkerdar
,
Reza Saeedi
,
Amin Bagheri
,
Mohammad Hajartabar
,
Mohammad Darvishi
,
Reza Saeedi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jhsw.v14i1.17124
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jhsw.v14i1.17124
Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance. Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems. Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%. The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%. Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.
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All OpenAlex metadata
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https://openalex.org/W4404888817Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18502/jhsw.v14i1.17124Digital Object Identifier
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Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive MaintenanceWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-12-01Full publication date if available
- Authors
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Masoumeh Khoshkerdar, Reza Saeedi, Amin Bagheri, Mohammad Hajartabar, Mohammad Darvishi, Reza SaeediList of authors in order
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https://doi.org/10.18502/jhsw.v14i1.17124Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.18502/jhsw.v14i1.17124Direct OA link when available
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Preventive maintenance, Health maintenance, Risk analysis (engineering), Engineering, Computer science, Reliability engineering, Construction engineering, Business, Health care, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.25.5% | 264 |
| abstract_inverted_index.5.7%. | 216, 273 |
| abstract_inverted_index.7.1%, | 194, 248 |
| abstract_inverted_index.These | 89 |
| abstract_inverted_index.after | 74, 159 |
| abstract_inverted_index.fines | 100 |
| abstract_inverted_index.heavy | 302 |
| abstract_inverted_index.human | 92 |
| abstract_inverted_index.lower | 312 |
| abstract_inverted_index.might | 280 |
| abstract_inverted_index.paper | 26 |
| abstract_inverted_index.rate, | 124 |
| abstract_inverted_index.shelf | 114 |
| abstract_inverted_index.state | 152 |
| abstract_inverted_index.study | 5, 62 |
| abstract_inverted_index.these | 144 |
| abstract_inverted_index.tire, | 118 |
| abstract_inverted_index.tires | 192, 246 |
| abstract_inverted_index.usage | 111 |
| abstract_inverted_index.using | 85, 143, 176 |
| abstract_inverted_index.which | 304 |
| abstract_inverted_index.would | 305 |
| abstract_inverted_index.14.6%, | 199, 253 |
| abstract_inverted_index.25.5%, | 208 |
| abstract_inverted_index.33.3%, | 186, 240 |
| abstract_inverted_index.Delphi | 87 |
| abstract_inverted_index.before | 72, 157 |
| abstract_inverted_index.better | 308 |
| abstract_inverted_index.costs. | 313 |
| abstract_inverted_index.domain | 136 |
| abstract_inverted_index.extend | 187 |
| abstract_inverted_index.health | 45 |
| abstract_inverted_index.impact | 29, 284 |
| abstract_inverted_index.mining | 20, 32, 78, 165, 178, 229, 277, 309 |
| abstract_inverted_index.reduce | 195, 290 |
| abstract_inverted_index.repair | 206, 262 |
| abstract_inverted_index.safety | 287 |
| abstract_inverted_index.showed | 219 |
| abstract_inverted_index.useful | 189, 243 |
| abstract_inverted_index.(TPMS), | 40 |
| abstract_inverted_index.(VHMS), | 48 |
| abstract_inverted_index.average | 256 |
| abstract_inverted_index.between | 69, 127 |
| abstract_inverted_index.domains | 103 |
| abstract_inverted_index.effects | 293 |
| abstract_inverted_index.enhance | 210 |
| abstract_inverted_index.failure | 123 |
| abstract_inverted_index.health, | 50, 105 |
| abstract_inverted_index.improve | 298 |
| abstract_inverted_index.include | 91 |
| abstract_inverted_index.method. | 88 |
| abstract_inverted_index.program | 271 |
| abstract_inverted_index.safety, | 51, 106 |
| abstract_inverted_index.systems | 39, 47, 145, 180, 227, 279 |
| abstract_inverted_index.vehicle | 44 |
| abstract_inverted_index.Material | 58 |
| abstract_inverted_index.Methods: | 60 |
| abstract_inverted_index.Results: | 168 |
| abstract_inverted_index.affected | 15 |
| abstract_inverted_index.assessed | 148 |
| abstract_inverted_index.decrease | 182 |
| abstract_inverted_index.downtime | 257 |
| abstract_inverted_index.examines | 27 |
| abstract_inverted_index.findings | 170, 218 |
| abstract_inverted_index.follows: | 234 |
| abstract_inverted_index.indicate | 174 |
| abstract_inverted_index.industry | 17 |
| abstract_inverted_index.machines | 230 |
| abstract_inverted_index.negative | 291 |
| abstract_inverted_index.obtained | 232 |
| abstract_inverted_index.positive | 283 |
| abstract_inverted_index.pressure | 37 |
| abstract_inverted_index.purpose, | 24 |
| abstract_inverted_index.reducing | 249, 254 |
| abstract_inverted_index.required | 204 |
| abstract_inverted_index.research | 65, 173 |
| abstract_inverted_index.systems, | 34, 42 |
| abstract_inverted_index.systems. | 80, 167 |
| abstract_inverted_index.warnings | 98 |
| abstract_inverted_index.Utilizing | 275 |
| abstract_inverted_index.accidents | 184, 238 |
| abstract_inverted_index.comparing | 150 |
| abstract_inverted_index.conducted | 68 |
| abstract_inverted_index.employing | 75 |
| abstract_inverted_index.equipment | 94, 183, 237 |
| abstract_inverted_index.failures, | 128 |
| abstract_inverted_index.including | 35 |
| abstract_inverted_index.intervals | 71 |
| abstract_inverted_index.machinery | 33, 79, 166, 179, 278 |
| abstract_inverted_index.machines, | 289 |
| abstract_inverted_index.overfill, | 120 |
| abstract_inverted_index.reduction | 235 |
| abstract_inverted_index.schedules | 133, 214 |
| abstract_inverted_index.specified | 155 |
| abstract_inverted_index.Initially, | 81 |
| abstract_inverted_index.accidents, | 93, 95 |
| abstract_inverted_index.compliance | 132, 213, 267 |
| abstract_inverted_index.conditions | 310 |
| abstract_inverted_index.identified | 84 |
| abstract_inverted_index.incidents, | 97 |
| abstract_inverted_index.increasing | 241, 266 |
| abstract_inverted_index.industry). | 21 |
| abstract_inverted_index.machinery, | 303 |
| abstract_inverted_index.monitoring | 38, 46 |
| abstract_inverted_index.parameters | 54, 82, 90, 156 |
| abstract_inverted_index.preventive | 130, 211 |
| abstract_inverted_index.technology | 13 |
| abstract_inverted_index.(especially | 18 |
| abstract_inverted_index.Conclusion: | 274 |
| abstract_inverted_index.consumption | 197, 251 |
| abstract_inverted_index.development | 11 |
| abstract_inverted_index.dispatching | 41 |
| abstract_inverted_index.intelligent | 31, 77, 164, 177, 226, 276 |
| abstract_inverted_index.investigate | 8 |
| abstract_inverted_index.maintenance | 131, 212, 270, 300 |
| abstract_inverted_index.parameters, | 112 |
| abstract_inverted_index.consumption, | 122, 296 |
| abstract_inverted_index.environment, | 109 |
| abstract_inverted_index.introduction | 161 |
| abstract_inverted_index.maintenance. | 57, 139 |
| abstract_inverted_index.preventative | 56, 138 |
| abstract_inverted_index.Introduction: | 0 |
| abstract_inverted_index.effectiveness | 141, 221 |
| abstract_inverted_index.environmental | 53, 96, 292 |
| abstract_inverted_index.descriptive-analytical | 64 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.67372586 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |