Scholarlink

<p>Effective risk analysis of dump truck failures is vital for enhancing reliability, minimizing downtime, and ensuring safety in mining operations. This study investigates failure modes of conventional dump trucks using three years of maintenance records from the Sarcheshmeh Copper Mine workshop, focusing only on mechanical and electrical failures (automation-related faults are outside the scope). Applying Pareto analysis and expert validation to key subsystems, a risk matrix was developed that identifies the engine as the dominant source of failures—accounting for 30% of incidents—followed by wheel and electrical systems. Primary engine failure mechanisms include power deficiency, coolant and oil leaks, and internal component malfunctions, all of which critically impair operational reliability. For each failure mode, targeted corrective actions were proposed (e.g., predefined sensor thresholds triggering alarms). Building on these findings, the study recommends tailored sensor and monitoring technologies to enable early fault detection and support preventive maintenance strategies for autonomous dump trucks. This sensor-based framework facilitates real‑time risk classification and effective mitigation. Grounded in empirical data, the proposed methodology provides a data‑driven, reproducible framework for improving safety and reliability, while acknowledging current limitations (static thresholds, autonomy‑failure exclusion) and future steps (predictive analytics).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Risk assessment of mechanical and electrical failures as a data‑driven foundation for alarming systems in autonomous dump trucks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

查看全文
  • E. Tarihi,
  • S. H. Hoseinie,
  • I. Izadi,
  • M. Monemi Gohari,
  • M. Mobed,
  • A. Jandaghi Jafari,
  • M. Rezaei Dashtaki

摘要

Effective risk analysis of dump truck failures is vital for enhancing reliability, minimizing downtime, and ensuring safety in mining operations. This study investigates failure modes of conventional dump trucks using three years of maintenance records from the Sarcheshmeh Copper Mine workshop, focusing only on mechanical and electrical failures (automation-related faults are outside the scope). Applying Pareto analysis and expert validation to key subsystems, a risk matrix was developed that identifies the engine as the dominant source of failures—accounting for 30% of incidents—followed by wheel and electrical systems. Primary engine failure mechanisms include power deficiency, coolant and oil leaks, and internal component malfunctions, all of which critically impair operational reliability. For each failure mode, targeted corrective actions were proposed (e.g., predefined sensor thresholds triggering alarms). Building on these findings, the study recommends tailored sensor and monitoring technologies to enable early fault detection and support preventive maintenance strategies for autonomous dump trucks. This sensor-based framework facilitates real‑time risk classification and effective mitigation. Grounded in empirical data, the proposed methodology provides a data‑driven, reproducible framework for improving safety and reliability, while acknowledging current limitations (static thresholds, autonomy‑failure exclusion) and future steps (predictive analytics).