Predictive Maintenance in Plants: What SOPs Miss

A small valve, a big save: what predictive maintenance catches that SOPs miss

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Standard operating procedures and interlocks are the backbone of safe, reliable plant operations. They codify best practices, enforce safeguards, and prevent known mistakes from cascading into incidents. Yet some failure modes still hide in plain sight—especially where human actions and manual configurations intersect with automated protections. A recent near-miss on a critical gear pump shows why predictive monitoring now belongs alongside SOPs and interlocks in every process plant.

The incident

A critical gear pump relied on a bearing cooling circuit to manage heat during operation. After scheduled maintenance, a manual valve in the cooling loop was inadvertently left closed. From the control room, everything looked healthy: interlocks were satisfied, permissives were met, and the pump was cleared to start.

What changed the outcome wasn’t in the DCS logic; it was on the asset. IoT sensors on the pump bearings immediately registered rising temperature and abnormal vibration—the telltale signatures of inadequate cooling. A targeted alert reached the maintenance team within hours. A technician inspected the pump, found the closed valve, reopened it, and stabilized the equipment. An expensive cascade of damage, downtime, and secondary impacts was avoided, all because the plant had a layer of predictive sensing that noticed what the interlocks could not.

Why SOPs missed it

Human error in manual configuration. The valve position was not digitally verified, and a simple oversight left a critical path closed.
Interlocks validated signals that didn’t include the manual valve state. Control logic saw permissives as “good” without visibility into this particular failure mode.
No immediate post-startup physical inspection was planned. A routine round might have found the heat rise later, but not before risk increased.

What this teaches

Early detection beats post-mortems. The difference between an inconvenient alert and a multi-day outage is often measured in minutes and hours, not weeks of root-cause analysis.
Redundancy must account for human factors. Sensing strategies should explicitly cover common manual errors—valves, bypasses, temporary jumpers, and local switches.
Predictive tools are not just for wear-and-tear. They also catch misconfiguration and procedural slips that sit outside traditional interlock coverage.

How to operationalize predictive reliability

Instrument the critical few

Assets: focus on pumps, compressors, gearboxes, blowers, and heat exchangers—the equipment that creates disproportionate risk and cost when it fails.
Signals: pair vibration and temperature with pressure differentials, flows, and motor currents. Together, they capture both mechanical health and process conditions.

Run analytics where they matter

Edge analytics for real-time alerts and resilience. Keep the most time-sensitive rules near the asset so detection works even during network hiccups.
Cloud analytics for fleet patterns and model updates. Use wider data to learn cross-site signatures, benchmark performance, and update models centrally.

Set clear alert logic

Start with physics-based thresholds and rates of change. Tie alarms to known limits and how quickly conditions deteriorate.
Layer in anomaly detection and prescriptive recommendations. Use learned baselines to reduce nuisance alarms and add guided next steps for technicians.

Close the loop

Route alerts to the right roles with expected actions and SLAs. A good alert includes who, what, and by when—not just a data point.
Document resolution and cause codes in the CMMS. Turn every incident into structured learning.
Feed confirmed outcomes back into models and SOPs. If a new failure pattern emerges, bake it into both analytics and procedures.

Make it stick

Share KPIs across maintenance, operations, and IT. Track time-to-detect, false-positive rate, avoided cost, and adoption rate as a single team.
Hold weekly triage reviews and monthly value tracking with finance. Turn anecdotes into verified savings and risk reduction.
Empower plant champions. Train peers, publish short win stories, and normalize data-driven decision making on the shop floor.

Scaling without losing momentum

A central reliability platform team should set standards for data, models, and governance, while plant teams adapt playbooks to local realities. Executive sponsors protect the time and budget; site leaders own adoption and outcomes. Most importantly, communicate in the plant’s terms—risk reduced, time saved, stops avoided—rather than abstract AI language. Predictive maintenance earns trust when it prevents losses operators recognize.

AI for production outcomes, defined

AI in production is not just “more product.” It’s fewer surprises, tighter variance in quality and throughput, safer startups and shutdowns, quicker troubleshooting, and higher personal productivity for operators and technicians. Those outcomes show up as smoother days, calmer shifts, and fewer midnight calls—observable benefits that anchor long-term change.

Practical next steps

Map the top 20 failure modes by cost and frequency. Start where risk concentrates.
Pilot on 3–5 assets that represent those modes. Prove the value path on a small, visible scale.
Establish a 90-day evidence plan. Set baselines, KPIs, and a cadence for post-incident reviews to validate avoided cost and refine logic.
Decide what to keep in-house versus partner. Providers like Infinite Uptime can accelerate prescriptive maintenance without adding long-term model upkeep to your teams.

The result

Plants that pair SOPs and interlocks with predictive monitoring see fewer avoidable breakdowns, better on-the-spot decisions, and stronger cross-functional alignment. Most importantly, the workforce trusts the data because it consistently prevents real losses—like a seized pump, a damaged bearing, or a forced outage—that everyone can picture.

A small valve left closed could have been an expensive lesson. Instead, it became proof that predictive sensing, clear alerting, and disciplined follow-through turn everyday oversights into fast, controlled recoveries. That is the future of reliability: not replacing human judgment, but equipping it with early, actionable insight.

Source: Insights from Rajneesh Ojha, Head of Digital Transformation, Indorama Ventures, at the CXO Circle event, Bangkok.

The trip to Thailand was sponsored by InfiniteUptime.