Engine Cooling System Degradation Detected in GHH LHD Loader

Engine overheating in underground loaders carries severe consequences, from cylinder head damage and blown gaskets to complete engine seizure, in an environment where recovery and replacement logistics are significantly more complex than surface operations. DataMind AI continuously monitors engine thermal parameters and cooling system health, enabling early detection before temperatures reach critical levels.

In this case, DataMind AI identified a sustained upward trend in engine coolant temperature on a GHH LHD Loader, with values repeatedly approaching the alarm threshold under load. Simultaneously, intake manifold temperatures were elevated, and the cooling fan duty cycle was already near maximum, indicating the system was working at capacity but failing to maintain thermal control. Coolant analysis returned normal results, ruling out fluid degradation.

Correlating the cooling fan load, coolant temperature trend, and intake temperature data, DataMind AI diagnosed degraded radiator heat rejection capacity as the root cause. The combination of maximum fan effort with insufficient cooling effect, alongside normal coolant condition, is a diagnostic signature of a mechanically compromised radiator, most likely from accumulated scale or external contamination blocking heat transfer surfaces.

Following the DataMind AI alert, the site team inspected the radiator and found significant scale buildup on internal surfaces. After flushing and cleaning, coolant temperature returned to normal operating range. Early detection prevented engine overheating damage, saved an estimated $35,000 in repair costs, and avoided 2 days of unplanned downtime.