Predictive Maintenance

Predictive Maintenance
with 4-Level Analytics

ROSSMA is more than vibration sensors. It is 4 levels of intelligent analytics: from basic telemetry to ML-powered RUL prediction. 21 diagnostic widgets, virtual flow meter (VFM), ASPO and gas slug detection, Decision Fusion for pipelines. Oil & gas, industry, energy

21 Widgets VFM RUL Prediction LoRaWAN Ex ISA-101
21 diagnostic widgets
Failure prediction 30–90 days
VFM without physical flowmeter
21
Analytics and diagnostic widgets
30–90days
Equipment failure prediction
<5%
False positives (Decision Fusion)
15%
Energy savings (VFD optimization)

Problems Solved by Predictive Analytics

Oil & gas, industry, and energy lose millions to emergency repairs

Unplanned Downtime

ESP, pump, or compressor failure stops production. Every hour of downtime means direct losses. Emergency repair costs 3-5x more than planned maintenance.

Schedule-Based, Not Condition-Based

Planned maintenance follows a calendar, not actual wear. Result: either overspending (early replacement) or failure (late replacement).

ASPO, Gas Slugs, Sand Production

Paraffin deposits, gas at pump intake, and mechanical impurities are the main causes of premature downhole equipment failure. Without analytics, discovered only at breakdown.

No Virtual Flow Meter

Physical flowmeters are expensive and unreliable on wells. Without VFM, production optimization is impossible — operators do not know real-time flow rates.

Pipeline Leaks

Traditional threshold systems detect only major leaks. Micro-leaks from 0.3% of flow and slow corrosion remain invisible for months.

No Data for Optimization

VFD frequency, pump jack stroke rate, and chemical dosing decisions are made without data. Missed savings: up to 15% on electricity alone.

4 Levels of ROSSMA Intelligent Analytics

From basic telemetry to ML prediction — each level adds intelligence to your data

4-Level Analytics

Level 1: basic telemetry, trends, threshold alarms. Level 2: well model and VFM. Level 3: full diagnostics (ASPO, gas slugs, leaks, sand). Level 4: ML-powered RUL prediction and regime optimization.

VFM — Virtual Flow Meter

Well flow rate calculation without a physical flowmeter. ESP accuracy ±5-15% (similarity laws, energy balance, pump curves). SRP accuracy ±12-25% (Gibbs method). Graceful degradation — works with any available sensor set.

21 Diagnostic Widgets

Pump and motor health score 0-100. ASPO index, gas slug detection, sand production, axial load, thrust bearing, power quality (THD, phase imbalance), energy efficiency, RUL prediction.

Decision Fusion for Pipelines

4 leak detection methods: NPW localization (±50 m, <60 sec), AI/ML Isolation Forest, hydraulic gradient, material balance. Less than 5% false positives.

Wireless Ex-Certified Sensors

ROSSMA IIOT-AMS: vibration, temperature, pressure, flow, gas analysis. LoRaWAN up to 15 km, battery up to 10 years. ATEX / TR CU 012/2011 Zone 0-1-2. Stainless steel IP67, -55 to +85 °C.

ROSSMA NETS HMI — ISA-101 SCADA

16 screens: P&ID mimics, trends, ISA-18.2 alarms (5 levels), 9 report types (Excel/PDF). Web interface with 3 themes. From 1 well pad to entire field (50-200+ devices).

Architecture: From Sensor to RUL Prediction

4 analytics levels — each adds intelligence to your data

Architecture: From Sensor to RUL Prediction Architecture: From Sensor to RUL Prediction

Economics: Reactive vs Scheduled vs Predictive

ROSSMA predictive maintenance delivers the lowest total cost of ownership

$30–50K
Cable savings per well pad
30–90 days
Failure prediction before workover
15%
Energy savings (VFD optimization)
Parameter Reactive Scheduled (PM) Predictive (ROSSMA)
Strategy Fix on failure Calendar-based Condition-based (CBM)
Failure prediction None None 30–90 days (ML)
Repair cost 3–5x planned Planned cost Minimal (early replacement)
Downtime Maximum Medium (over-maintenance) Minimal
Virtual flow meter None None VFM ±5–15%
ASPO / gas / sand At breakdown At breakdown Early diagnostics

Key Economic Advantages

Failure prediction 30–90 days ahead — preventing unplanned workovers
VFM without physical flowmeter — real-time production optimization
15% energy savings through VFD frequency optimization
$30–50K cable savings per well pad (wireless infrastructure)
Reduced spare parts cost — replacement based on actual wear, not schedules

Frequently Asked Questions

What is 4-level analytics?
Level 1: basic telemetry — data collection, trends, threshold alarms. Level 2: well model — VFM calculates flow without a physical meter. Level 3: full diagnostics — health score 0-100, ASPO, gas slugs, leaks, sand. Level 4: predictive analytics — ML models predict RUL, optimize regimes, forecast production.
How does VFM work without a physical flowmeter?
VFM calculates flow from indirect data. For ESP: similarity laws, energy balance, pump curves — accuracy ±5-15%. For SRP: Gibbs method and empirical current analysis — ±12-25%. Graceful degradation works with any sensor set. Auto-calibration from test separator data.
What is Decision Fusion for pipelines?
Four independent leak detection methods (NPW pressure wave, AI/ML Isolation Forest, hydraulic gradient, material balance) combined via weighted voting. Result: less than 5% false positives, detects leaks from 0.3% of flow, localization accuracy ±50 m.
What equipment can be monitored?
Oil & gas: ESP, SRP, test separators, gathering manifolds, transformers, pipelines. Industry: pumps, compressors, fans, gearboxes, conveyors, motors. Energy: transformers, generators, heat exchangers.
Do sensors work in hazardous areas?
ROSSMA IIOT-AMS Ex sensors are ATEX / TR CU 012/2011 certified for Zone 0, 1, 2. Protection: Ex d (flameproof) and Ex ia (intrinsically safe). IP66/IP67 stainless steel, -55 to +85 °C.
How far in advance are failures predicted?
RUL models predict workover date 30-90 days ahead. ASPO, gas slug, and sand diagnostics detect issues 2-8 weeks in advance. NPW pipeline analysis detects leaks in under 60 seconds.
What are the 21 analytics widgets?
VFM, pump health (0-100), motor health (0-100), ASPO index, gas slugs, sand production, axial load, thrust bearing, power quality (THD, phase imbalance), energy efficiency, RUL prediction, production forecast, leak detection, and more.
What is the implementation cost?
Cable savings: $30-50K per well pad. Per monitoring point: from ~$200 (sensor + gateway share + software). Deployment: 2-3 weeks per pad. ROI: preventing one unplanned workover covers the entire system cost.

Ready for Predictive Analytics?

Get a system quote with VFM, RUL prediction, and Decision Fusion for your facility — from one well pad to an entire field

Office

9D Chkalova St., office 320, 322 Perm, Perm Krai Russia, 614064

Manufacturing

9 Chkalova St., building 3 Perm, Perm Krai Russia, 614064

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