Plant problems
From Reactive to Predictive Maintenance: A Realistic Roadmap
You do not start with machine learning. You start with clean breakdown records. A staged path that works in Indian plants.
Trikaan Research & Engineering4 min read
KEY TAKEAWAYS
- You do not start with machine learning. You start with clean breakdown records.
- Each stage has prerequisites; skipping them wastes money.
- Focus condition monitoring on critical machines with known failure modes.
- Measure progress with MTBF, MTTR and planned vs unplanned work.
Predictive maintenance is often sold as a sensor-and-AI project. In practice, plants that succeed get there in stages, each building on the data and habits of the previous one.
Here is the roadmap we recommend.
Stage 1: Reactive, but recorded
Every breakdown recorded with machine, component, failure mode, cause, repair time and waiting time. Without this history, nothing later works.
Stage 2: Preventive
Scheduled tasks — lubrication, inspection, replacement — based on time or running hours, with checklists and completion tracking. The breakdown history tells you which tasks matter.
Stage 3: Condition-based
Measure condition on critical machines — vibration, temperature, current, pressure — and act when readings cross limits. Start with the bottleneck and machines whose failure is expensive.
Stage 4: Predictive
With condition data and labelled failure history, models can detect patterns that precede failure and estimate how much time remains. Alerts become work orders planned into low-impact slots.
Prerequisites at a glance
| Stage | You need | Typical first win |
|---|---|---|
| Recorded reactive | Simple structured logging | See top failure causes |
| Preventive | Asset list, task schedules | Fewer wear failures |
| Condition-based | Sensors or PLC signals on critical assets | Earlier warning on key machines |
| Predictive | Months of signals plus failure labels | Planned interventions days ahead |
Building the business case
Maintenance investments compete with production and capital projects for budget. Make the case with numbers from your own records: hours of unplanned downtime on the bottleneck, the production value of those hours, spare-part expediting costs and overtime for repairs. Then estimate the share that a staged programme can realistically prevent.
Start with one critical machine family and prove the result before scaling. A successful pilot on the expellers or the main compressor is far more persuasive than a plant-wide plan.
People and process
- Technicians trained to record failures consistently.
- Clear priority rules between breakdown and planned work.
- Stores aligned on critical spares.
- Production willing to release machines for planned work.
Measuring progress
- MTBF rising on critical machines.
- MTTR falling, with waiting time separated.
- Share of planned vs unplanned maintenance work.
- Unplanned downtime on the bottleneck.
Frequently asked questions
How long does it take to reach predictive maintenance?
It depends on starting data. Plants with good breakdown records can add condition monitoring within months; models need enough history to learn from.
Which machines should get sensors first?
The bottleneck and machines whose failure is costly or dangerous.
Do we need a CMMS?
A system to schedule and record maintenance is essential; it can be part of a wider platform.
Can existing PLC data be used?
Often yes — motor current, temperatures and run status are frequently available.
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Trikaan Research & Engineering
Trikaan
Written by the researchers and engineers who build Anvaya, ASTR and Seed Track, with input from Trikaan's delivery and engineering teams.
