We benchmark your asset base, failure history, and maintenance regime, then engineer an autonomous AI-enabled TPM system — moving plants from reactive, to preventive, to predictive, to fully autonomous closed-loop operation.
Preventive maintenance saves equipment from running to failure but pays for it with over-maintenance, unnecessary downtime, and induced faults. Our Digital TPM service replaces calendar-based regimes with condition-based regimes informed by AI — predicting when an asset will need attention and prescribing the work order, the parts, and the technician skill required.
A digital TPM system is not an analytics dashboard. It is an end-to-end closed loop: sensors capture asset condition, edge models score health and remaining-useful-life, predictions flow into the EAM/CMMS to generate work orders, work orders are completed and the outcomes are fed back to retrain the models. We build the entire loop.
A working catalogue of the systems, models, and platforms our engineers ship within this practice — selected for industrial reliability, observability, and scale.
Multi-modal health indices per asset class — pumps, motors, compressors, gearboxes, conveyors — built from physics + ML.
Probabilistic RUL models with calibrated uncertainty supporting risk-weighted maintenance decisions.
Models distinguishing degradation modes (bearing, imbalance, looseness, cavitation) to prescribe correct intervention.
Direct integration with SAP PM, IBM Maximo, Infor EAM converting predictions into prioritized, parts-ready work orders.
Demand forecasting for spare parts driven by predictive failure rates — right inventory at the right depot.
MTBF, MTTR, Weibull, FMECA — modernized with ML and self-updating from live operational data.
Representative deployments across our industrial client base. Each grounded in production engineering — not concept slides.
Vibration + current + temperature fusion identifies bearing wear, cavitation, and motor degradation 4-8 weeks ahead of failure across hundreds of compressors per site.
DGA + thermal + load history models predict insulation degradation and partial-discharge events, transforming utility O&M from reactive replacement to scheduled overhaul.
Acoustic + thermal + amperage telemetry on long mining and material-handling conveyors detects idler failures and belt damage before unplanned shutdowns.
Thermal performance modelling predicts fouling progression and optimizes cleaning schedules in refineries and chemical plants — saving energy and avoiding unscheduled outages.
Joint-current and encoder-deviation models on robot fleets predict gearbox wear and end-of-arm tooling drift, scheduling preventive recalibration before quality slips.
Force-curve and acoustic monitoring on press lines and resistance welders flag tooling wear cycles ahead of dimensional or weld-quality drift.
Across distributed real-estate portfolios, AI scoring on chillers, AHUs, and pumps reduces energy waste and emergency callouts while extending equipment life.
Failure-rate forecasts feed automated spares replenishment per depot, slashing stockouts and emergency airfreight while reducing total carrying cost.
Tools, frameworks, and platforms our engineers use day-to-day in this practice.
Every Pentaaxis engagement starts with a structured benchmarking conversation — no obligation, a senior engineer in the room, and a calibrated view of where AI moves the needle for your organization.