Our own product · live demo
Boljam — predictive maintenance for refining and oil production
Boljam is a predictive maintenance system built by 105 Industrial AI. It brings vibration diagnostics, telemetry and maintenance history into one loop: five machine-learning models classify defects, estimate remaining useful life and detect anomalies before RMS velocity crosses the ISO threshold. On a demo fleet of 450 machines the detector leads the threshold on 377 units, with a median lead time of 13 days. It opens in a browser and runs without a server round-trip.
- days of median lead time over the RMS thresholdacross 377 machines in the demo fleet
- 13
- pieces of equipment under monitoring1,302 measurement points, 147 MW
- 450
- ROC-AUC of the anomaly detectorfalse-alarm rate 0.001
- 0.977
- months of history — scrub with the cursorsignal recomputed in the browser
- 18
A bearing 85% destroyed — instruments still read “as new”
A pump at 1476 rpm with an 85% outer-race defect: RMS velocity of 1.86 mm/s — zone A under ISO 20816-3. Formally the machine is healthy. Meanwhile the envelope at the defect frequency has grown 95-fold and kurtosis has gone from 2.96 to 10.87. That is exactly what Boljam surfaces.
Demonstration on synthetic data: the figures come from a physics model, not from measurements on real equipment. Asset names are taken from public sources; the system is not the reporting of any company and uses no corporate branding.
Two assets in one loop
Refinery
refining, 5.5 mln t/year, three production units
- 16 process units — from a 1969 crude distillation train to a 2018 deep-conversion complex
- 267 machines, 709 measurement points, 89.0 MW
- Pumps, compressors, flue-gas fans, air coolers, furnaces
Mature oil field
production, four field units, waterflood system, oil treatment
- 183 machines, 593 measurement points, 58.4 MW
- 3,698 wells: 92.5% rod pumps, 7.5% ESP; 121 under vibration monitoring
- Rod-pump diagnostics from dynamometer cards — 8 condition classes
How it works
Three engineering decisions that make the demo fast, honest and server-free.
The signal is synthesised, not stored
No raw waveforms are stored anywhere. A physics model compiled from Rust to WebAssembly generates them deterministically in the browser from a key of tag + point + time. The same code drives the offline dataset generator, so stored features always agree with the signal you see when you drag the cursor to any date.
Consistency verified end to end
The median discrepancy in RMS between the stored dataset and the browser recomputation is 0.0000%, with a 95th percentile of 0.17%. Two generator runs produce bit-identical files. The DSP core is covered by 62 tests, the domain logic by 184 unit tests plus end-to-end scenarios.
Inference in the browser, data stays put
Training is offline and one-off; inference runs client-side through ONNX Runtime Web, with all models totalling 1.76 MB. The same architecture transfers to an enterprise perimeter: computation happens where the data sits and nothing leaves the site.
Five models and their measured quality
Train/test splits are by machine identity rather than by window, so there is no target leakage: the ISO zone is explicitly excluded from the inputs.
| Model | Task | Measured quality |
|---|---|---|
| FaultNet | Defect classification, 12 classes | Recall 0.94 at precision 0.85–0.92 |
| RULNet | Remaining useful life P10/P50/P90 | RMSE 66.9 days, interval coverage 0.804 against a nominal 0.80 |
| HealthIndex | Anomaly detector | ROC-AUC 0.977, false alarms 0.001 |
| DynaCard | Rod-pump condition from dynamometer cards, 8 classes | Accuracy 0.993 |
| Reliability | Reliability, optimal maintenance interval | Weibull with censoring, agreement by Kaplan–Meier |
What the system cannot do — stated plainly
FaultNet detects bearing defects, cavitation, looseness and gear mesh, but it does not separate imbalance, misalignment, blade-pass and electromagnetic faults: their energy sits on the first running harmonics, below the demodulation band, and physically never reaches the envelope spectrum. Those classes are diagnosed from first-harmonic and electrical features instead. All models are trained on synthetic data; validation on public recordings has not been completed, and the interface says so directly. Loaders for public datasets (CWRU, NASA IMS, MFPT, Paderborn, XJTU-SY, Petrobras 3W, MetroPT-3, C-MAPSS) are included — validation on real data is performed during the customer pilot.
Standards base
ISO 20816-3 — zones A/B/C/D by RMS velocity, with the band following shaft speed: from 2 Hz below 600 rpm, otherwise from 10 Hz. ISO 20816-8 and ISO 10816-6 cover reciprocating machines, where limits are 28–45 mm/s rather than 4.5–11. API 670 covers turbomachinery on sleeve bearings, where the governing criterion is relative shaft displacement rather than casing RMS. Also: ISO 13373, ISO 17359, ISO 13379, ISO 13381, API 610.
What it gives an enterprise
For the chief mechanic and reliability team
A ranked risk list across the whole fleet instead of a calendar round: you see which machines need intervention in the next 7 and 30 days and why — with the defect frequency and its development over time.
For planning and economics
The economic effect is computed on screen rather than in a slide deck: all 25 assumptions are displayed with a rationale for each, so the calculation can be challenged item by item and rebuilt with your own downtime and repair costs.
For IT and information security
No database, no outbound calls: computation happens in the browser. In an industrial setting the same architecture is deployed on-premise — telemetry and video never leave the plant perimeter.
The same approach — delivered as a service on your equipment
Predictive maintenance serviceFAQ
Frequently asked questions
What chief engineers, security and procurement teams usually ask — with straight answers.
Next step
See Boljam applied to your own fleet
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