Artificial Intelligence-Driven Predictive Maintenance for Hydro Turbines: TurbineSafe Feasibility Study in Georgia

This article discusses TurbineSafe — a Georgian startup project that offers a predictive maintenance system for hydro turbines based on IoT sensors and artificial intelligence. The high precipitation characteristic of Georgian rivers reduces the efficiency of turbines of hydropower plants by 15-30% and causes losses of 50,000-300,000 GEL per plant annually. TurbineSafe solves this problem by synthesizing four types of sensors, edge computing and a cloud AI module (LSTM neural network, up to 95% accuracy) — it warns the operator about a possible malfunction 60 days in advance. The SaaS subscription model makes the system 10 times more accessible than global analogues. Market analysis (SAM: 150-200 million GEL) and GREDA survey (70% demand) confirm a solid commercial basis. The article also discusses the technical architecture of the system, business model, team structure and national energy significance in the context of the Georgian Energy Development Plan 2034.