Predictive vs preventive maintenance: what's the difference, and what's the ROI?
Preventive maintenance runs on a fixed schedule, whether or not a fault is present: it reduces failure risk but also triggers unnecessary work on equipment that's in perfectly good shape. Predictive maintenance, by contrast, relies on the machine's actual condition — vibration, temperature, current — and only steps in when the data calls for it. Across the fleets Monixo monitors, this approach cuts unplanned downtime while avoiding needless maintenance work. Value builds progressively, asset by asset, as the historical data deepens. The switch doesn't happen overnight: it starts with instrumenting a limited number of critical assets, long enough to build the data history that lets the models sharpen their accuracy.
Why 88% of industrial failures are avoidable
Most failures on an industrial fleet aren't one-off events: they're recurring failure modes that show up, in similar form, across different pieces of equipment. An imbalance, a misalignment, a bearing working loose — these signatures are known and detectable well before the break. That's the premise behind data-driven maintenance: instead of discovering a failure the moment it happens, the goal is to recognise, in the vibration or thermal signal, the early signs of an already-documented failure mode. The challenge isn't only technological — installing sensors — it's organisational too: folding that information into day-to-day maintenance practice, so it turns into concrete action rather than an alert nobody acts on.
Water resilience in West Africa: the role of IoT data
In West Africa's major cities, drinking-water networks often rely on dozens of boreholes and pumping stations, some installed decades ago. The challenge isn't a lack of water — it's a lack of visibility into the real condition of that infrastructure. By instrumenting every submerged pump — vibration, temperature, current, insulation, water-table level — without ever stopping it, it becomes possible to detect degradation weeks before failure, where it previously went entirely unnoticed. That's the approach structured with SEN'EAU: qualify a multi-parameter architecture, standardise measurement points, then progressively extend monitoring to priority infrastructure.
predictive maintenance & CBM