Specialist Condition Monitoring at Anglo American

Job Description:

• Support the implementation and monitoring the Condition Monitoring Strategy by working closely with sites and business unit teams.
• Closely collaborate with asset designers on condition based maintenance requirements, sensing techniques, and sensor types.
• Advise on CM technique that is application driven (e.g. vibration, torque, etc.).
• Oversee the implementation of new measuring devices and systems, ensuring devices are correctly installed and properly tested.
• Conduct training related to devices/systems to site teams and other stakeholders.
• Project manage sensor and system implementations.
• Ensure that data is continuously collected data from assets that are monitoring online.
• Support the implementation of the Condition Monitoring Strategy based on Reliability Centered Maintenance Principles.
• Conduct data analysis and derive insights like patterns/trends from data.
• Conduct asset tagging to ensure unique identification.
• Establish baseline data for all critical assets for key failure modes.
• Inform Reliability Engineers, Site Maintenance and Production personnel on condition monitoring exceptions.

Qualifications:

• An undergraduate qualification (Bachelors / Honours degree or equivalent) in a relevant technical discipline.

Technical Knowledge:

• Specialist understanding and application of Condition Monitoring in the mining and resource industry.
• Reliability Engineering fundamentals.
• Sound understanding of the full mining/ resource extraction value chain and its key drivers – from the ore body through the extraction, processing, infrastructure and mining.
• Ability to work collaboratively with site Reliability Engineers to set expectations and create alignment.
• Knowledge of relevant technology and data analytics in order to draw insights and recommendations.
• Experience in the application of technical expertise in Condition Monitoring in large scale mining operations.
• Provides consistent outstanding behaviour in relation to safety practices with a deep understanding of the importance of safety.
• Applies a systematic, knowledge-based problem-solving approach and structured methodologies (such as root cause analysis and statistical analysis) to investigate risks and opportunities to create effective, value-add technical solutions.
• Strong machine learning knowledge base and intermediate level of understanding of the different machine learning algorithms that can be applied to CM.
• Strong IT knowledge to ensure connectivity of all CM systems and devices, including IIOT, cloud computing, etc.