Predictive maintenance software helps you spot failure signs early using real equipment data. Our IoT predictive maintenance solution on Petran turns those signals into clear alerts and planned actions. See it on your assets.
If you’re looking for predictive maintenance software or comparing predictive maintenance solutions, this is for teams that want fewer breakdowns and more planned work. It uses AI and IoT predictive maintenance data to catch risk early and act before downtime.
Industrial predictive maintenance software helps you spot early warning signs and prioritize the right fixes, instead of chasing emergencies.
AI + IoT based predictive maintenance helps you avoid surprise line stops and plan maintenance around production.
A predictive maintenance software system helps you standardize monitoring and decision making across plants.
Teams usually start looking for predictive maintenance software when downtime keeps happening even with PM in place. These are the common reasons people evaluate AI + IoT predictive maintenance solutions.
Preventive maintenance is time based. Failures are condition based. Industrial predictive maintenance software helps catch early signs between PM cycles.
Signals sit in PLC/SCADA, historians, and sensors, but nobody has time to connect them. A predictive maintenance software system should turn data into a clear next step.
When alerts are noisy, they get ignored. The best predictive maintenance software prioritizes risk and reduces alarm fatigue.
Without early warning, parts and manpower are arranged late. Predictive maintenance solutions help you plan shutdown work before it becomes urgent.
Predictive maintenance software is really about one thing: giving you time before a failure turns into downtime. Not a bigger checklist. Just earlier clarity.
Our predictive maintenance solutions use Petran and Tritva to spot small changes in equipment behavior and turn them into something your team can act on.
With early warnings and Remaining Useful Life (RUL) estimates, leadership can shift maintenance from reactive firefighting to scheduled work. That means fewer unplanned outages, better utilization of maintenance windows, and improved production adherence because interventions are planned around operational priorities.
Predictive signals let you focus resources on the assets most likely to fail soon and most costly to stop. For senior management, this translates into measurable outcomes: reduced lost production hours, fewer secondary damages from catastrophic failures, optimized spare parts spend and improved OEE/asset availability through fewer major disruptions.
Ombrulla designs AI solutions aligned with your strategic priorities whether it’s automating operations, improving customer experiences, or unlocking insights from data. Built for scale and ROI, our AI empowers leaders to reduce costs, accelerate growth, and gain a competitive edge.
When teams invest in predictive maintenance software, they usually want fewer surprises and more control. These are the outcomes we focus on with our predictive maintenance solutions using Petran and Tritva.
You stop replacing parts “just in case” and start fixing what is actually drifting. With industrial predictive maintenance software, teams usually see less emergency overtime, fewer repeat repairs, and better spare planning.
IoT predictive maintenance helps you catch problems early enough to repair during planned windows. That means fewer line stops, more stable output, and fewer last minute decisions that affect quality.
A good predictive maintenance software system keeps a clear trail: what was detected, who acted, what was done, and what improved. This helps during audits and reduces the risk of missed maintenance on critical assets.
You do not need to instrument everything on day one. Start with your most critical assets, use existing data where possible, and expand step by step. That approach is how most predictive maintenance solution providers deliver value faster with less disruption.
A good predictive maintenance software system should work on one line, and still work when you expand across plants. These capabilities help our predictive maintenance solutions stay usable day to day, not just during reviews.
Pull the right signals from sensors, PLC/SCADA, historians, and inspections. Clean it, tag it to assets, and keep the context, so teams trust the output.
When conditions change during a shift, you need fast visibility. IoT predictive maintenance helps spot abnormal behavior early, before it becomes a breakdown.
Not everyone wants raw graphs. Health indicators summarize risk clearly, so leaders and engineers can align on what needs attention first.
RUL (Remaining Useful Life) tells you how long an asset can likely run before risk becomes serious. It helps plan spares, manpower, and shutdown windows.
Instead of only saying “something changed,” the system can suggest likely failure modes (for example, imbalance, misalignment, bearing wear). That saves troubleshooting time.
Technicians need practical guidance. What to check first, what readings confirm it, and what action usually fixes it. That’s how alerts turn into work.
Insights should not stay in dashboards. Push actions into CMMS/EAM as work orders, track completion, and link results back to the original alert.
Role based access, audit trails, and deployment options that fit your IT policies. This matters when you roll predictive maintenance across multiple teams and sites.
If you’re comparing predictive maintenance solution providers, the real question is simple: will this actually work on your plant floor, and will your team use it? Ombrulla’s predictive maintenance software system is built to make AI + IoT predictive maintenance practical, not complicated.
Bring any of these. Even one is enough.
Connect with our experts to discuss how AI inspection can work for you.
Predictive maintenance software uses equipment condition data to warn you before failures happen, so maintenance can be planned based on risk.
Preventive maintenance is time based. Predictive maintenance is condition based and uses real signals to decide when service is needed.
AI learns normal behavior from IoT and control system signals, detects abnormal changes, and sends alerts with context so teams can act early.
Start with assets that cause high downtime or safety risk: pumps, motors, compressors, fans, gearboxes, conveyors, and key utilities.
Basic signals like vibration, temperature, current, pressure/flow, speed, runtime, and alarms. PLC/SCADA or historian data is often enough for a pilot.
Not always. Many plants start with existing PLC/SCADA and historian signals, then add sensors only where needed.
Yes. You can start with anomaly detection. Confirmed failures and maintenance outcomes improve accuracy over time.
Use multi signal checks, confidence scoring, asset specific baselines, and tuning after early feedback from technicians.
Yes. Integration helps push actions into work orders and closes the loop from alert to maintenance completion.
A pilot can start quickly once data access is ready. Scaling happens step by step across similar assets and lines.
Track avoided downtime, emergency work orders, planned vs reactive work, MTBF/MTTR, alert precision, and availability/OEE trends.
What should we share before a demo?
