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About

Hamid Shiri is a researcher specializing in condition monitoring, fault detection, and robotic inspection systems, with a particular focus on industrial applications in mining and bulk material handling. His work sits at the intersection of signal processing, machine learning, and autonomous robotics, addressing one of mining's most persistent challenges: the reliable, safe monitoring of belt conveyor idlers and their critical rolling element bearings. Shiri's most impactful contribution — garnering 48 citations — introduced a robotic unmanned ground vehicle (UGV) platform capable of acoustic signal-based fault detection in belt conveyor idlers, a system that dramatically reduces human exposure to hazardous rotating machinery. Building on this foundation, his subsequent research has explored comparative vibro-acoustic analysis methods, real-world acoustic diagnostics under challenging mining conditions, and advanced heterogeneous information fusion techniques that combine multiple sensor streams through ensemble classifiers with dynamic weighted voting. What distinguishes Shiri's research is its practical orientation: rather than laboratory-bound solutions, he consistently targets deployment in harsh, real-world industrial environments. His development of automated, robot-assisted diagnostics represents a meaningful step toward safer, more efficient mining operations, positioning him as an emerging voice in intelligent condition monitoring and industrial inspection robotics.