Predictive equipment maintenance
Machine-learning models trained on vibration diagnostics, telemetry and maintenance history predict failures of pumps, compressors, turbines and gas-pumping units days to weeks before an incident. The result: 20–40% fewer unplanned outages and a shift from time-based preventive maintenance to condition-based maintenance driven by the actual state of each machine.
What problems it solves
Pain
Unplanned failures of critical equipment: a single submersible pump failure means 3–10 days of well downtime and $30–100K in losses
Solution
Anomaly detectors on telemetry time series plus remaining-useful-life (RUL) models with root-cause explanation (SHAP)
30–40% of emergency repairs become planned repairs
Pain
Calendar-based preventive maintenance: some repairs are unnecessary, others come too late
Solution
A ranked “risk list” of the equipment fleet for the next 7/30 days with a recommendation: change the operating regime or schedule a planned shutdown for repair
−10–15% maintenance budget, longer mean time between repairs
Pain
“Second-tier” equipment (balance-of-plant pumps, blowers) is not covered by expensive vendor monitoring systems
Solution
Model-free anomaly detectors that auto-tune to each machine, plus low-cost wireless vibration sensors where there is no telemetry
−20–30% unplanned repairs on second-tier equipment
What data we work with
- Process control (DCS/PLC) and SCADA telemetry (OPC UA, Modbus), historians (PI, WinCC)
- Vibration diagnostics and pump control-station data
- Maintenance history from CMMS/EAM (SAP PM, 1C)
- Operating parameters and laboratory data
What the customer gets
- Data readiness audit and business-case calculation (2–3 weeks)
- A working model on the customer's real data with quality metrics (8–12-week pilot)
- Economic-effect report: failures prevented × cost per failure
- Industrial roll-out plan with CMMS integration and SLA support
What problem predictive maintenance solves
Time-based preventive maintenance services equipment by the calendar rather than by its actual condition: some repairs are done for nothing, while sudden failures still happen. On an oil field, one electric submersible pump (ESP) failure costs $30–100K including the repair and lost production; across a stock of 2,000–4,000 wells that is $15–40 million a year in losses at a single oil and gas production unit.
Meanwhile the telemetry (current, pressure, vibration, temperature) is already collected by the process control system at most plants — but it is used reactively, after the incident. Predictive analytics turns the same data into early warnings.
Comparison: time-based vs condition-based maintenance
| Criterion | Time-based (calendar) | Predictive (condition-based) |
|---|---|---|
| Basis for repair | Regulatory interval | Actual degradation of the component |
| Unplanned failures | Not prevented | −20–40% |
| Unnecessary repairs | 30–50% of work is redundant | Eliminated |
| Maintenance budget | Fixed, grows with wear | −10–15% through prioritization |
| Planning horizon | Reactive | 7–30 days before failure |
- Criterion
- Basis for repair
- Time-based (calendar)
- Regulatory interval
- Predictive (condition-based)
- Actual degradation of the component
- Criterion
- Unplanned failures
- Time-based (calendar)
- Not prevented
- Predictive (condition-based)
- −20–40%
- Criterion
- Unnecessary repairs
- Time-based (calendar)
- 30–50% of work is redundant
- Predictive (condition-based)
- Eliminated
- Criterion
- Maintenance budget
- Time-based (calendar)
- Fixed, grows with wear
- Predictive (condition-based)
- −10–15% through prioritization
- Criterion
- Planning horizon
- Time-based (calendar)
- Reactive
- Predictive (condition-based)
- 7–30 days before failure
Which equipment this applies to
- Pumps (ESP, sucker-rod pumps, multistage centrifugal pumps, balance-of-plant pumps)
- Compressors and gas-pumping units
- Turbines and turbine-generator sets
- Power transformers (forecast from dissolved gas analysis (DGA) trends)
- Open-pit mining equipment (from VHMS/VIMS onboard telemetry)
- Locomotives and rolling stock
What economic effect predictive analytics delivers
The formula: (number of unplanned failures prevented × average cost per failure) + (longer mean time between repairs × lower repair costs) + reduced production shortfall. A realistic range for a large production asset is $3–8 million a year. After successful pilots at the Atyrau and Pavlodar refineries, KazMunayGas is scaling predictive analytics in 2026 to 100+ critical assets at each plant.
Implementation stages
| Stage | Duration | Result |
|---|---|---|
| Data audit | 2–3 weeks | Assessment of telemetry and maintenance-history readiness, estimate of the potential effect |
| Pilot | 8–12 weeks | Model on 50–200 machines, quality metrics, effect report |
| Industrial roll-out | 4–9 months | Whole fleet, CMMS integration, staff training, SLA |
- Stage
- Data audit
- Duration
- 2–3 weeks
- Result
- Assessment of telemetry and maintenance-history readiness, estimate of the potential effect
- Stage
- Pilot
- Duration
- 8–12 weeks
- Result
- Model on 50–200 machines, quality metrics, effect report
- Stage
- Industrial roll-out
- Duration
- 4–9 months
- Result
- Whole fleet, CMMS integration, staff training, SLA
FAQ
Frequently asked questions
What chief engineers, security and procurement teams usually ask — with straight answers.
Next step
Request a data audit
Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.
- 1A 30-minute call: the task, the data, who signs off on security
- 2Data audit or a retrospective analysis on an anonymised export
- 3An 8–12 week pilot with success criteria agreed before the start
NDA from the first contact
Reply within one business day