Articles and technical notes ·
Predictive maintenance from SCADA and RTU data: process control starts with the measurements you already have
How to turn trend data you already collect into an early warning of failure, without a large AI project.
Predictive maintenance does not start with an algorithm; it starts with data. SCADA systems and RTUs already collect measurements such as temperature, current, pressure and battery voltage. Read as a trend rather than a momentary value, they reveal degradation before the equipment fails.
Simple examples: a steady rise in transformer oil temperature; pump current that creeps up at a constant flow; falling battery voltage on a solar remote unit before the site goes silent; a growing pressure difference across a filter. In each case the trend alert arrives well before the out-of-range alarm.
Starting steps: check data quality (timestamps, units, gaps in reporting); set a baseline for each asset under normal operation; alert on the rate of change, not only on a fixed threshold; and record every alert and what was found, so the rule can be tuned.
The technical literature already offers examples, including a 2026 study that tested fault detection in photovoltaic systems on SCADA data from an operating solar plant. Accuracy figures quoted by vendors, however, vary widely between sites, so test any method on your own data before relying on it.
For a pilot on one installation, contact the Teleris engineering team. This article is general and does not promise any particular result.