Turnaround and Shutdown Predictive Maintenance Planning Checklist
By Ethan Walker on June 15, 2026
Plant turnarounds and shutdowns are the highest-stakes, highest-cost events in industrial maintenance — a single week-long shutdown at a mid-sized refinery or chemical plant typically consumes $5–$25 million in direct maintenance expenditure, with production loss adding another $2–$10 million in gross margin impact per day of extended duration. The difference between a turnaround that finishes on time and on budget and one that overruns by 30–50% is not the quality of the contractor workforce or the adequacy of the spare parts inventory — it is the completeness and accuracy of the predictive maintenance data that feeds the scope definition, work package sequencing, and repair-or-replace decisions made during the planning phase. Traditional turnaround planning relies on fixed-interval inspection findings from the previous operating cycle — opening vessels, pulling heat exchanger bundles, and performing visual inspections that reveal only the conditions severe enough to be visible to the naked eye. In contrast, a PdM-driven turnaround uses continuous sensor data from the preceding 12–24 months of operation — vibration trends, corrosion probe readings, wall thickness measurements, valve signature analysis, and lubrication analysis — to identify the specific assets with developing faults, quantify their remaining useful life, and make evidence-based repair-or-run decisions that eliminate unnecessary work while catching failure modes that visual inspection would miss entirely. This checklist covers the complete PdM-driven turnaround planning framework — from data collection strategy through scope optimization, work package sequencing, spare parts validation, and post-turnaround model retraining — structured for reliability engineers and turnaround planners who need a repeatable process for integrating predictive intelligence into the shutdown planning cycle. Book a Demo to see how iFactory AI connects your PdM data to turnaround planning workflows.
Predictive Maintenance-Driven Turnaround and Shutdown Planning
Condition-based scope definition · PdM-driven work package sequencing · Remaining useful life for repair-or-run decisions · All flowing into iFactory CMMS & Shift Logbook.
Why Traditional Turnaround Planning Misses Critical PdM Signals
Turnaround planning in most industrial plants follows a well-established cycle: the planning team reviews the previous turnaround's work scope, incorporates new regulatory inspection requirements, adds reactive work orders generated during the operating cycle, and schedules fixed-interval component replacements based on OEM recommendations. The missing input is the continuous condition monitoring data generated during the 12–24 months between turnarounds — vibration trends showing developing bearing spalls, corrosion probe readings indicating accelerated wall loss, valve signature trends revealing seat leakage progression, and lubrication analysis tracking wear particle generation rates. A bearing that will reach end-of-life three months after the planned restart does not need replacement during the turnaround — but a bearing that has already entered Stage 3 spalling and will fail within 30 days of restart must be replaced regardless of its scheduled replacement interval. Without PdM data integration, the planning team cannot distinguish between these two cases. The result is a work scope that is simultaneously over-scoped (replacing bearings that still have months of useful life) and under-scoped (missing assets that will fail weeks after restart because their PdM data was never reviewed during the planning phase).
PdM DATA GAPS IN TRADITIONAL TURNAROUND PLANNING
1
Vibration trend data excluded — bearing condition trends from the operating cycle reviewed only if specific work requests were raised, not systematically mined for scope definition
2
Corrosion monitoring siloed — UT wall thickness data and corrosion probe readings held by the inspection team, not integrated with mechanical work scope planning
3
Valve stroke data unconnected — partial stroke test results and valve signature trends available in DCS but never cross-referenced against turnaround valve overhaul lists
4
Lubrication analysis unused — oil sample analysis trends showing wear particle generation rates not factored into gearbox overhaul decisions despite being the best indicator of internal gear condition
Condition-Based Scope Definition from Continuous Monitoring Data
The most impactful PdM contribution to turnaround planning is the systematic conversion of continuous monitoring data into work scope items. iFactory ingests vibration trends, corrosion probe data, valve signature analysis, lubrication analysis, and thermography records from the preceding operating cycle and cross-references each asset's condition trajectory against its projected run time to the next planned shutdown. Assets with developing faults that will reach failure threshold before the next planned shutdown are flagged for corrective scope inclusion. Assets currently within acceptable limits but showing accelerating degradation rates are flagged for conditional scope — open, inspect, and repair-or-run decision based on in-shutdown findings. Assets with stable condition trends are excluded from scope unless regulatory or reliability-based replacement intervals require otherwise. This systematic approach typically reduces turnaround work scope by 15–25% while catching 30–50% more developing faults than traditional planning methods. Book a Demo to see iFactory's condition-based scope definition in production.
15-25% scope reduction30-50% more faults caughtEvidence-based repair-or-run
02
PdM-Prioritized Work Package Sequencing
Work package sequencing in traditional turnaround planning is driven by critical path logic and resource availability alone — the sequence in which vessels are opened, bundles pulled, and components inspected follows a schedule set months before the shutdown. iFactory overlays PdM severity data onto the sequencing logic, enabling the planning team to prioritize work packages based on the probability of finding actionable defects. Assets whose PdM trends show high probability of requiring repair or replacement are scheduled earlier in the turnaround window to allow maximum time for remediation. Assets whose PdM trends show stable condition are scheduled later, with shorter planned durations reflecting the lower likelihood of finding defects requiring extended repair. This PdM-prioritized sequencing reduces schedule overrun risk by enabling the turnaround team to focus the early, high-availability portion of the shutdown on the assets most likely to require intervention.
The most expensive decision in turnaround planning is replacing a component that still has useful life remaining — the cost includes not only the replacement part and labour but the unnecessary consumption of critical-path shutdown time. iFactory's RUL models, trained on degradation trajectories from vibration trends, corrosion rates, and wear particle analysis, provide quantitative remaining life estimates for each asset entering the turnaround window. An asset with an RUL of 18 months on a 24-month turnaround cycle does not need replacement. An asset with an RUL of 6 months on the same cycle requires replacement. These estimates are continuously updated as new monitoring data accumulates during the operating cycle, ensuring the turnaround scope reflects the most current condition trajectory, not the condition measured 12 months ago when the last vibration survey was taken.
Spare parts availability is a perennial constraint in turnaround execution — the wrong bearings, seals, or gaskets in inventory while the right ones are on backorder is the most common cause of mid-shutdown expediting and work-around decisions that compromise quality. iFactory cross-references the PdM-identified scope items — bearings flagged for replacement, valves showing seat wear, pumps with impeller degradation — against the current spare parts inventory, generating a gap analysis that identifies every part required for the PdM-driven scope that is not currently in stock. This analysis is typically completed 8–12 weeks before the turnaround window, providing sufficient lead time for procurement or repair of long-lead items. The same analysis validates that parts currently in inventory but not required for any PdM-identified scope are not unnecessarily ordered again.
8-12 week lead timeInventory gap analysisEliminated duplicate ordering
05
Regulatory Inspection Scope Optimization
Regulatory inspection requirements — API 510 vessel inspections, API 570 piping inspections, API 653 tank inspections — represent a fixed scope element that cannot be eliminated. However, the extent of inspection within each vessel or piping circuit can be optimized using PdM data. A pressure vessel with stable corrosion probe readings and consistent wall thickness measurements across the previous two operating cycles may qualify for reduced inspection extent under API 510 risk-based inspection principles, while a vessel with accelerating corrosion trends requires full internal inspection regardless of its scheduled interval. iFactory's platform integrates corrosion monitoring data, UT wall thickness records, and process conditions into a risk-based inspection optimization engine that maximizes inspection effectiveness per hour of turnaround time by focusing the inspection team on the circuits with the highest probability of finding actionable degradation.
Post-Turnaround Baseline Reset and Model Retraining
Every turnaround changes the condition baseline of the plant's equipment — replaced bearings, overhauled valves, re-tubed heat exchangers, and reconditioned pumps all start the new operating cycle with condition signatures that differ from their pre-turnaround state. iFactory's platform captures every repair and replacement action executed during the turnaround from the CMMS and Shift Logbook, automatically resetting the condition baselines and degradation trajectory models for each affected asset. Vibration spectra collected on the first post-restart run are compared against post-repair acceptance criteria rather than against the pre-turnaround baseline, ensuring that condition monitoring trends reflect actual degradation from the new baseline rather than being corrupted by the condition change introduced by the turnaround repairs. Models are retrained on post-turnaround data to ensure prediction accuracy for the new operating cycle.
Automatic baseline resetPost-repair acceptance criteriaModel retraining for new cycle
07
Turnaround Effectiveness Metrics and Continuous Improvement
Every turnaround generates a wealth of data about which PdM predictions were accurate, which assets were replaced earlier than needed, and which developing faults were missed by the monitoring program. iFactory captures these outcomes systematically — comparing pre-turnaround PdM predictions against actual in-shutdown findings and post-restart performance — to generate turnaround effectiveness metrics that drive continuous improvement in both the PdM program and the planning process. Metrics include PdM prediction accuracy per asset class, avoided unnecessary replacements based on RUL confidence, scope inclusion accuracy (how many PdM-flagged assets actually required intervention), and post-restart failure rate for assets that were run based on RUL estimates rather than replaced. These metrics are documented in the Shift Logbook and reviewed in the post-turnaround reliability review, forming the evidence base for improving PdM model accuracy and turnaround planning effectiveness for the next cycle.
Turnaround PdM Planning — Key Data Sources and Integration Points
The effectiveness of PdM-driven turnaround planning depends on the breadth and quality of monitoring data available for integration. iFactory connects to the full range of data sources typically available in process plants, refineries, and chemical facilities — vibration databases, corrosion monitoring systems, UT wall thickness records, valve signature analysis platforms, lubrication analysis databases, thermography records, and DCS historian data — and consolidates them into a single turnaround planning workspace. The table below maps each data source to the turnaround planning workflow it supports, the typical integration method, and the lead time required for data readiness before the turnaround planning window opens.
Data Source
PdM Turnaround Workflow
Integration Method
Data Readiness Lead Time
Vibration Database
Bearing and gearbox condition for scope definition and RUL estimation
API connector to vibration software (CSI, Emerson, Bently Nevada)
4 weeks — data extraction and trend validation
Corrosion Monitoring System
Wall loss rates for pressure vessel and piping inspection optimization
API connector to corrosion database or UT data management system
4 weeks — probe data validation and trend calculation
Valve Signature Analysis
Valve seat leakage, stem wear, and actuator condition for overhaul scope
Data import from valve testing platform or DCS partial stroke test logs
2 weeks — data export and format mapping
Lubrication Analysis
Wear particle trends for gearbox and bearing condition validation
API connector to LIMS or data import from oil analysis laboratory
2 weeks — trend calculation and threshold definition
DCS Historian
Process conditions for RBI optimization and corrosion rate correlation
OPC UA or API connector to OSIsoft PI, AspenTech, or Siemens historian
4 weeks — data extraction and alignment with monitoring data
CMMS
Work order history for failure pattern analysis and spare parts validation
API connector to SAP, Oracle, IBM Maximo, or Infor EAM
6 weeks — data extraction, cleaning, and cross-referencing
Turnaround PdM Planning — Implementation Timeline and Milestones
Integrating predictive maintenance data into turnaround planning is not an overnight process. The following phased timeline shows the recommended sequence of activities, milestones, and decision gates for a plant planning its first PdM-driven turnaround. Each phase has defined deliverables that build on the previous phase's outputs, ensuring that the PdM integration progresses from data readiness through scope definition to execution support and post-turnaround learning.
Phase 1
Data Readiness & Baseline
12–8 weeks pre-turnaround
Complete data extraction from all PdM sources — vibration, corrosion, valve, lubrication, DCS, and CMMS. Validate data completeness and quality. Generate asset-level condition summaries with degradation trends and RUL estimates. Identify data gaps that require supplemental inspection or sensor deployment before turnaround.
Deliverable
Data readiness report · Asset condition baseline · Gap analysis
Phase 2
Scope Definition & Optimization
8–4 weeks pre-turnaround
PdM data systematically mined for scope items — flag assets reaching failure threshold before next planned shutdown, identify assets with accelerating degradation for conditional scope, exclude assets with stable trends. Cross-reference PdM scope against regulatory requirements and previous turnaround history. Generate optimized work scope with evidence-based repair-or-run recommendations.
Deliverable
PdM-optimized work scope · Evidence package per asset · Repair-or-run matrix
Phase 3
Execution Support & Post-TA
During turnaround + 4 weeks post
PdM data packages support in-shutdown repair-or-run decisions as assets are opened and inspected. Post-repair acceptance criteria enable quick verification of work quality. After restart, condition baselines are reset for all repaired assets, and models are retrained on post-turnaround data. Turnaround effectiveness metrics compiled and reviewed.
Deliverable
Post-turnaround baseline · Model retraining · Effectiveness metrics report
Want to run a PdM-driven turnaround planning pilot on your next shutdown? Book a Demo to walk through your specific turnaround scope with our team and identify the highest-value PdM integration points for your next shutdown.
15-25%
Turnaround work scope reduction
Eliminate unnecessary replacements with RUL-based decisions
The recommended lead time for first-time PdM data integration into turnaround planning is 12 weeks before the turnaround start date. This allows 4 weeks for data extraction and quality validation across all PdM sources (vibration, corrosion, valve signature, lubrication, DCS historian, and CMMS), 4 weeks for PdM-driven scope definition and optimization, and 4 weeks for spare parts procurement and work package finalization. For subsequent turnarounds where the data infrastructure and integration workflows are already established, the lead time can be reduced to 8 weeks. iFactory recommends starting the data readiness phase no later than 12 weeks before turnaround to allow sufficient time for data gap resolution — installing additional sensors on assets identified as having inadequate monitoring coverage, for example — before the planning window closes.
iFactory integrates with your existing turnaround planning tools, CMMS (SAP, Oracle, IBM Maximo, Infor EAM), and data historians — it does not replace them. The platform ingests PdM data from your existing condition monitoring systems, generates scope recommendations and RUL estimates, and writes the resulting work packages and evidence packages back to your CMMS through standard API connectors. The turnaround planning team continues using their existing tools and workflows; iFactory adds the PdM intelligence layer that ensures scope decisions are grounded in actual equipment condition data rather than fixed intervals or tribal knowledge. The Shift Logbook provides the operational interface for capturing turnaround execution data — work completed, findings, repair details — that feeds back into model retraining for the next operating cycle.
Borderline assets — those whose PdM data shows elevated but not critical condition indicators, or whose RUL estimate falls within the uncertainty range of the next planned shutdown timing — are flagged as conditional scope items in iFactory's turnaround planning workspace. These assets are assigned a conditional work package that includes specific inspection criteria to be performed during the turnaround, with a decision tree based on the inspection findings. For example: "Open bearing housing. If visual inspection shows spalling on more than 30% of raceway surface OR if post-shutdown vibration measurements exceed 7.1 mm/s, replace bearing. Otherwise, clean, regrease, and run to next planned turnaround with increased monitoring frequency." This conditional scope approach ensures that borderline assets are inspected during the turnaround but are not automatically replaced, preserving the efficiency gain of PdM-driven scope optimization while maintaining the safety net of in-shutdown verification for assets where the RUL estimate has the widest confidence interval.
The typical ROI for PdM-driven turnaround planning is 4–8× the investment in data integration and platform costs, realized primarily through three quantifiable savings streams. First, work scope reduction of 15–25% from eliminating unnecessary replacements — for a $10 million turnaround, this represents $1.5–$2.5 million in direct cost avoidance. Second, extended turnaround intervals enabled by confidence in PdM-based condition assessment — plants that demonstrate reliable RUL-based decision making across two operating cycles can justify extending turnaround intervals from 36 months to 48 months, reducing total turnaround cost by 25% over the extended period. Third, avoidance of post-restart failures caused by missed pre-turnaround defects — each major post-restart failure on an asset that should have been identified during PdM data review costs $100,000–$500,000 in emergency repair costs and production loss during the restart and ramp-up phase. An ROI modeling session using your specific turnaround scope, cost structure, and PdM data availability is available at no cost.
Deploy iFactory for PdM-Driven Turnaround Planning
AI-powered predictive maintenance platform connecting vibration data, corrosion monitoring, valve signature analysis, lubrication analysis, and DCS historian data into a unified turnaround planning workspace — with condition-based scope definition, RUL-driven repair-or-run decisions, spare parts validation, and post-turnaround model retraining. Integrated with Shift Logbook, CMMS, and existing condition monitoring systems.
