In the mining sector, where production delays can ripple through global supply chains and downtime often translates to millions in lost revenue, operational reliability is paramount. Yet, one of the most pervasive – and frequently overlooked – threats to uptime is dirty power.
Often invisible until it causes catastrophic failure, dirty power refers to any irregularity in a facility’s electrical supply, including voltage sags, harmonic distortion, transients and frequency instability. While these issues may appear benign on the surface, they can erode the performance of mission-critical equipment, damage sensitive electronics, and significantly shorten the lifespan of high-value assets such as pumps, drives and conveyor systems.
Go deeper with GlobalData
As mining operations become more digitalised and automated, maintaining clean, stable power is no longer just an engineering concern – it’s a business imperative. Increasingly, forward-thinking mining companies are turning to AI-powered predictive maintenance to stay ahead of the problem.
Why is dirty power a growing problem in mining?
Mining environments are uniquely susceptible to power quality issues. Remote locations, long feeder lines and harsh electrical loads from high-powered machinery all contribute to power instability. Add in increasing reliance on variable frequency drives (VFDs), motor soft starters and switch-mode power supplies, and the risk of harmonic distortion and voltage fluctuations grows exponentially.
The consequences of dirty power in mining operations include premature equipment failure; erratic performance of automated systems, unexpected downtime; and potentially wasted maintenance budgets that don’t address root cause.
In many cases, these problems are difficult to diagnose. Known in maintenance circles as “ghost faults”, they often manifest sporadically – during shift changes, equipment startups, or under specific loading conditions – making them elusive and expensive to troubleshoot.
US Tariffs are shifting - will you react or anticipate?
Don’t let policy changes catch you off guard. Stay proactive with real-time data and expert analysis.
By GlobalDataThe benefits of AI-supported maintenance
Traditional maintenance strategies rely on fixed schedules, inspections and reactive troubleshooting. However, dirty power events do not follow predictable timelines. Additionally, they can originate from unpredictable sources: loose connections in site substations, harmonic interference from a cluster of VFDs, or transient events during utility switching.
This is where AI-driven predictive maintenance offers a distinct advantage.
By continuously monitoring electrical signals at the waveform level, AI systems can detect patterns that indicate early signs of degradation — long before alarms are triggered or equipment fails. Machine learning algorithms trained on historical data and real-time performance metrics can distinguish between normal fluctuations and emerging faults.
The business case
For mining operators grappling with deeper ore bodies, energy-intensive operations and tightening budgets, predictive maintenance offers more than just peace of mind. It directly contributes to operational efficiency and cost control.
- Reduced downtime: Avoiding a single day of unplanned shutdowns can justify the entire predictive maintenance initiative.
- Extended asset life: By catching electrical anomalies early, operators can prevent the types of stress that lead to early equipment retirement.
- Lower maintenance costs: Instead of blanket replacements or reactive fixes, targeted interventions reduce unnecessary labour and parts usage.
- Improved safety: Minimising equipment failures reduces risks to workers and ensures compliance with safety standards.
AI’s greatest value lies not just in detection but in contextualization – understanding not just what is happening, but why it is happening and what to do next. In a mining operation with complex power systems, this context is essential for making smart, timely interventions.
Implementing AI-supported monitoring systems
While the benefits of AI-powered power monitoring systems are substantial, mine operators should be aware of several key considerations before implementation.
Barriers such as high upfront costs, infrastructure readiness, workforce skill gaps and data connectivity challenges, especially in remote or underground environments, can impact deployment timelines and overall return on investment.
Interoperability with existing mining systems and equipment is another hurdle, as legacy assets may lack compatibility with modern digital tools.
That said, the value of these systems goes beyond basic power monitoring. Solutions that leverage continuous waveform capture and electrical signature analysis provide deeper insights into the mechanical health of mining equipment.
By translating electrical waveforms into harmonic spectrums, these systems can detect early signs of mechanical wear such as broken teeth on cutting heads, loose or catching conveyor belts, or failing drive components long before visible symptoms arise. This enables predictive maintenance and helps reduce unplanned downtime.
For mining companies exploring adoption, it is recommended to start with a pilot programme focused on high-value or failure-prone assets.
Partnering with vendors that offer both technical support and training can ease the transition and close skills gaps. Ensuring reliable underground connectivity, whether wired or wireless, is also critical.
Choosing a solution with edge processing capabilities can help in areas where bandwidth is limited.
Ultimately, successful implementation depends on aligning the technology with operational goals, safety standards and change management strategies across the organisation.
A strategic shift towards intelligent maintenance
Mining is no stranger to innovation – from autonomous haul trucks to smart drilling systems – but electrical infrastructure is often left behind. As operations become more electrified and automated, maintaining clean, reliable power will only grow in importance.
The shift toward AI-powered predictive maintenance represents a strategic evolution. It moves electrical reliability from an afterthought to a core part of production planning. It allows teams to focus not just on fixing problems but on preventing them entirely. It also empowers mining companies to protect their most valuable assets – not just their equipment but their uptime, workforce and bottom line.
Dirty power may be invisible, but its impact on mining operations is real and measurable. By leveraging AI-driven predictive maintenance strategies, mining companies can bring clarity to chaos, mitigate risk and build more resilient operations.
About the author: Denis Kouroussis is a PhD computer engineer and serial entrepreneur who co-founded Atom Power, creator of the first UL-listed solid-state circuit breaker. Now CEO of Volta Insite, he leads the development of advanced electrical signature analysis predictive maintenance technology to detect issues early, prevent downtime and transform power system reliability.
