Human reliability analysis in maintenance and repair operations of mining trucks: A Bayesian network approach.

Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness.

Bayesian network approach for reliability analysis of mining trucks.

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output.

Reliability analysis of the compressed air supplying system in underground mines.

Despite the high cost and low efficiency, compressed air is mostly used in underground mining for ore extraction, hoisting, and mineral processing operations. Failures of compressed air systems not only threaten the health and safety of workers but also contribute to inefficient control of airflow and stopped all equipment that operates by compressed air. In such uncertain conditions, mine managers are faced with the big challenge to supply enough compressed air, and therefore, the reliability evaluation of these systems is essential.

Prioritization of practical solutions for the vibrational health risk reduction of mining trucks using fuzzy decision making.

The goal of this article was to prioritize the practical solutions for vibrational health risk reduction of truck drivers during mining operation using the multicriteria decision-making (MCDM) techniques. Mining trucks require special consideration because of their specific suspension system, large size, payload capacity, and off-road conditions of mining. In most cases, it is not easy for decision makers to compute verbal and linguistic variables, whose values are expressed in linguistic terms.