Sensor Maintenance: The Barrier to Full Automation in Bulk Material Handling

From ports to power plants, steel mills to storage terminals, the relentless push for efficiency, uptime, and safety is fuelling the demand for smarter, more automated systems.

Automated systems like stacker-reclaimers and ship loaders boost efficiency by enabling continuous, high-speed operations with precise material handling, reducing downtime and human error. They enhance safety by minimising manual labour in hazardous environments, using sensors and controls to prevent accidents like collisions or overloading. Additionally, real-time monitoring and data analytics optimise performance, ensuring consistent throughput while maintaining strict safety standards. 

While the spotlight of automated systems often lands on robotics, AI, and autonomous vehicles, there’s one crucial component that could make or break automation ROI – sensor technology and their maintenance demands.

The Bigger Picture: Automation Beyond Mining 

Traditionally associated with mining, automation is now transforming bulk material handling across virtually every heavy industry. The global bulk material handling systems market was valued at $28.1 Billion in 2024 and is forecasted to grow at a CAGR of 6.5% from 2026 to 2033, reaching $45.3 Billion by 2033*. 

But there’s a catch: automation depends entirely on the ability of sensors to perceive the environment – accurately, reliably, and without interruption. 

Lidar vs. Radar: More Than Just Vision 

Sensor technology is, in essence, the “eyes” of automated systems. In recent years, both lidar and radar have emerged as frontrunners in helping machines navigate, detect hazards, and monitor conditions in real time. Lidar’s edge lies in its high-resolution 3D mapping, which is ideal for precision tasks such as inventory management, structural modelling, or spatial analysis. 

However, as any operator in a Ship Loader or Stacker Reclaimer will tell you, environments filled with dust, moisture, or vibration create a maintenance nightmare for lidar sensors. Lidar units, often placed in hard-to-reach or hazardous zones, need frequent manual cleaning, recalibration or complex cleaning and dust suppression technologies, that add cost and maintenance to the system. Collectively this introduces downtime and, more ironically, a regression in the autonomous loop.

And that’s the paradox. 

We’re trying to remove people from dangerous, repetitive tasks, yet are requiring them to maintain the very tools designed to protect them. 

Radar: The ‘Fit-and-Forget’ Alternative 

Enter millimetre-wave radar, a technology that’s proving far more resilient in rugged industrial conditions. Unlike lidar, radar is unaffected by dust, fog, rain, or ambient light – conditions that frequently plague bulk operations. Its robust performance means that once installed, radar systems require minimal to zero ongoing maintenance, reducing both total cost of ownership and human intervention. 

This isn’t theoretical. Radar’s practical benefits are already being seen across sectors: 

• Accurate volumetric measurements in Stockpiles: Radar sensors ensure precise measurement of irregular stockpile volumes, optimising resource management in mining operations. 

• Bucket wheel cut profiling: Understanding material profiles pre- and post-cut, enables mining automation engineers to optimise cutting parameters, for greater efficiencies. 

• Underground Mining Wall detection for automation: In dusty, subterranean environments where cameras fail, wall detection ensures safe manoeuvring of large, automated machinery. 

These use cases go beyond mining and speak to the broader need for uninterrupted operations in mission-critical infrastructure. 

The Real Cost of Downtime 

According to a Deloitte report**, poor maintenance strategies reduce productivity by 5% to 20%, costing global manufacturers an estimated $50 billion annually. 

In bulk material handling, unplanned downtime can halt entire supply chains. A failure in a Port Conveyor System or a Ship Loader being down, or out of action, might mean delays in exports, fines, or lost contracts. The math is simple: when the data stops, so does production. 

This is why sensor maintenance is no longer a footnote – it’s a strategic factor. Relying on human labour to regularly clean, replace, or calibrate sensors is a step backward in the autonomy journey. Worse, it reintroduces risk, error, and cost into systems designed to be smart, safe, and self-sufficient.