Machine Health Specialist

The Machine Health Monitoring Specialist is responsible for overseeing the performance, diagnostics, and health status of mining equipment from a remote monitoring centre. This role involves the continuous monitoring of machine performance data, identifying issues, and ensuring the optimal operation of machinery, thereby minimizing downtime and extending equipment life in mining operations.

Ideally, we are looking for mechanical engineers with hands-on experience maintaining mobile equipment such as underground trucks, loaders, drills, and similar heavy machinery.

Key Responsibilities:

· Continuous Monitoring & Data Analysis:

o Monitor real-time data from machine health sensors, IoT devices, and diagnostic systems installed on mining equipment.

o Use remote monitoring software and predictive analytics tools to track equipment performance, identifying anomalies, early signs of wear, and maintenance needs.

o Maintain and manage a dashboard for equipment health, status, and operational alerts.

· Diagnostics & Fault Detection:

o Interpret data and trends to detect mechanical faults, wear and tear, and operational inefficiencies.

o Provide detailed analysis and recommendations for corrective action based on data insights, including vibration analysis, temperature monitoring, and fuel usage.

o Collaborate with on-site maintenance teams to ensure timely troubleshooting and resolution of machine health issues.

· Predictive Maintenance:

o Implement and improve predictive maintenance models to forecast equipment failures before they occur.

o Work closely with the maintenance team to schedule maintenance activities based on predicted failure data, minimizing unscheduled downtime.

o Review and adjust predictive models based on performance data and historical trends.

· Reporting & Documentation:

o Prepare regular reports on equipment health, performance trends, and any significant issues for management review.

o Document all maintenance activities, diagnostics, and corrective actions taken, ensuring compliance with operational standards and safety protocols.

o Track KPIs related to equipment uptime, failure rates, and maintenance effectiveness.

· Collaboration with On-site Teams:

o Act as a point of contact for site-based personnel regarding equipment health and maintenance schedules.

o Provide remote support and guidance to technicians and operators on-site, particularly in diagnosing and resolving complex equipment issues.

o Ensure that site teams are well-informed about maintenance priorities and urgent equipment issues.

· Technology & System Management:

o Ensure that all monitoring systems, sensors, and software tools are fully operational and integrated with mining equipment.

o Liaise with IT support teams to maintain the health of monitoring software and hardware infrastructure.

o Stay updated on technological advancements and integrate new solutions to improve equipment monitoring and predictive maintenance capabilities.

· Health, Safety, and Compliance:

o Ensure that all monitoring activities adhere to safety regulations and industry standards, including maintaining compliance with environmental regulations.

o Contribute to continuous improvement initiatives focused on safety, operational efficiency, and cost reduction.

o Participate in safety audits and provide input to improve equipment safety features based on health monitoring data.

Key Performance Indicators (KPIs):

· Machine Uptime: Percentage of equipment uptime versus downtime due to machine failures.

· Predictive Maintenance Accuracy: Percentage of maintenance actions taken proactively based on predictive maintenance signals.

· Fault Detection Time: Average time taken from detecting a fault to resolving it.

· Cost Reduction: Reduction in unplanned maintenance costs through effective remote monitoring and early detection.

· Data Integrity & Accuracy: Accuracy of diagnostic reports and data quality from monitored equipment.

· Health & Safety Compliance: Number of safety incidents related to equipment health and monitoring errors.