Leveraging IIoT to Improve Machine Safety in the Mining Industry.

Area of Science:

• Occupational Safety and Health
• Industrial Engineering
• Internet of Things (IoT)

Background:

• Machinery-related accidents pose significant risks to mine workers annually.
• Inadequate or improper use of lockout/tagout (LOTO) procedures is a common factor in these incidents.
• There is a critical need for advanced safety methods to monitor hazardous areas, document maintenance, and prevent unexpected machinery startup.

Purpose of the Study:

• To introduce the National Institute for Occupational Safety and Health's (NIOSH) research on applying Internet of Things (IoT) technologies.
• To develop an intelligent machine monitoring system integrated into a comprehensive LOTO program.
• To mitigate machinery-related accidents in mining operations through enhanced safety protocols.

Main Methods:

• Implementation of a two-phase IoT-based intelligent machine monitoring system.
• Phase one: Installation of a proof-of-concept system at a concrete batch plant.
• Phase two: System scale-up with additional sensors, safety/performance metrics, proximity detection, and predictive failure analysis.

Main Results:

• The study details the design and planned implementation of an IoT system for LOTO procedures.
• Proof-of-concept deployment will validate system functionality in a real-world mining environment.
• Future phases aim to enhance monitoring capabilities and introduce predictive maintenance features.

Conclusions:

• IoT technology offers a promising solution for intelligent machine monitoring within LOTO programs.
• The proposed system aims to improve safety by enhancing hazard monitoring and maintenance oversight.
• This research seeks to significantly reduce machinery-related accidents and improve worker safety in the mining industry.