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    Industrial AI solution

    Predictive Maintenance AI For Manufacturing Plants

    Predict failures on motors, pumps, compressors, conveyors and CNC machines before they happen. Integrated with SCADA, historian, SAP PM and CMMS.

    Plan the use case Explore ROI tools
    Predictive maintenance AI on a manufacturing plant floor

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram builds predictive maintenance AI for manufacturing plants that forecasts equipment failures on motors, pumps, compressors and CNC machines using vibration, current and SCADA data, integrated with SAP PM, Maximo and your CMMS to auto-create work orders before failure.

    Private deployment available

    Why evaluate it

    Stop Reactive Maintenance. Predict Failures Before They Happen.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    We deploy production-grade PdM on your plant - not a slide deck. Edge inference on PLC or industrial PC, models trained on your historian data, and bidirectional integration with SAP PM, Maximo or IBM Maximo so work orders are created automatically when failure risk crosses a threshold.

    Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    Vibration & current signature analysis

    FFT-based feature extraction from vibration and motor-current data to detect bearing wear, misalignment, imbalance and cavitation before they cause failure.

    • Triaxial vibration at 10-25 kHz from IFM / SKF / Banner sensors
    • Motor current signature analysis (MCSA) for rotor and stator faults
    • Envelope spectrum analysis for bearing defect frequencies
    • Edge feature extraction to minimise bandwidth to historian

    Failure prediction & remaining useful life

    Per-asset ML models that learn normal operating signatures and predict failure probability and remaining useful life with calibrated confidence intervals.

    • Autoencoder and LSTM anomaly models for each asset class
    • Survival analysis for remaining useful life estimates
    • Failure-mode classification (bearing, seal, coupling, lubrication)
    • Calibrated probability scores - not just alerts

    CMMS work-order automation

    Bidirectional integration with SAP PM, Maximo or your CMMS so predicted failures create work orders automatically with recommended parts, procedures and technician skills.

    • Auto-create notifications and work orders in SAP PM / Maximo
    • Recommended spare parts pulled from BOM
    • Skill and crew assignment based on failure mode
    • Closed-loop feedback: technician outcome retrains the model

    Plant-wide reliability dashboards

    Site-level visibility into asset health, MTBF, MTTR and avoided downtime so plant managers and reliability engineers can prioritise capital and labour.

    • Asset criticality matrix with live health scores
    • MTBF / MTTR trending by asset class and line
    • Avoided downtime $ tracking against baseline
    • Drill-down from plant to line to asset to sensor

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Data acquisition

    • OPC UA / OPC DA

      Standard SCADA connectivity

    • Kepware / Ignition

      Industrial data gateway

    • PI Historian / Ignition Historian

      Time-series source of truth

    • Vibration sensors (IFM, SKF, Banner)

      Triaxial accelerometers

    ML & analytics

    • PyTorch / TensorFlow

      Failure prediction models

    • AWS Lookout for Equipment

      Managed PdM service

    • Azure Anomaly Detector

      Cloud anomaly scoring

    • Prophet / NeuralProphet

      Trend forecasting

    Edge & deployment

    • NVIDIA Jetson

      Edge inference at the asset

    • Docker / K3s

      Edge orchestration

    • MQTT / Sparkplug B

      Lightweight telemetry

    Workflow integration

    • SAP PM

      Auto-create work orders

    • IBM Maximo

      CMMS notifications

    • ServiceNow

      Operations ticketing

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    Which assets benefit most from predictive maintenance AI?+
    Rotating equipment - motors, pumps, compressors, fans, gearboxes and conveyors - delivers the strongest ROI because vibration and current signatures are highly predictive. CNC spindles, hydraulic systems and HVAC are also strong candidates. Static equipment like heat exchangers and tanks need different sensor strategies (ultrasonic, thermal) but are still addressable.
    How much historical data do we need to start?+
    This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.
    Do we need to add new sensors?+
    Often no. Most plants already have temperature, pressure, flow and motor-current data in their historian. We start with what you have, then recommend targeted vibration or ultrasonic sensors on the 5-10 highest-criticality assets where the ROI justifies the investment.
    How does this integrate with our SAP PM or Maximo?+
    Bidirectional. Our models push predicted-failure notifications and work-order recommendations into SAP PM (via OData or BAPI) or Maximo (via REST API). When the technician closes the work order, the outcome and root cause feed back into the model for continuous learning.
    Where does inference run - cloud or edge?+
    Both. Heavy training runs in your cloud or on-prem GPU cluster. Inference can run at the edge (NVIDIA Jetson or industrial PC next to the PLC) for sub-second latency and to keep raw vibration data on-site, or in the cloud for less latency-sensitive assets. Architecture is decided per asset class.
    What ROI should we expect and how long to value?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project