How much data is needed before models are useful?

Anomaly-detection baselines are typically usable within a few weeks of continuous telemetry per asset class. Failure-mode classifiers require labelled failure events; physics-based rules and OEM signatures cover the interim so useful outputs are available from day one.

Which CMMS and EAM platforms are supported?

IBM Maximo, SAP Plant Maintenance, Infor EAM, Oracle eAM, UpKeep and Fiix are supported through bi-directional integration for work-order creation, spare-parts reservation and closure feedback, aligned to the customer's existing work-management processes.

How is the business case for reliability measured?

Avoided downtime, spare-parts consumption and maintenance-hour reduction are measured per asset class using consistent baselines. Dashboards distinguish measured outcomes from modelled estimates so reliability, operations and finance see the same numbers.

How is a candidate failure mode chosen before a model is trained?

Reliability engineers, plant operators and data teams review failure history, downtime cost and detectability, so modelling effort targets modes with clear early signals and material business impact rather than the most convenient dataset.

How are false positives kept low enough for maintenance teams to trust alerts?

Models are tuned against a defined precision and recall target, with feedback loops from work-order outcomes retraining thresholds, so alert fatigue is managed and interventions are triggered on genuine early warnings.

How do condition-based alerts flow into the CMMS work-order process?

Alerts create prioritised notifications in Maximo, SAP PM, Infor EAM or equivalent, with recommended tasks, spares and skills attached, so planners schedule interventions inside the existing maintenance operating model.

IoT & Automation

Predictive Maintenance

Move from calendar-based to condition-based maintenance.

Design, integration and support cover predictive-maintenance programmes covering vibration, acoustic, thermal, infrared and fluid sensing, unsupervised anomaly detection, supervised failure-mode models and remaining-useful-life estimation. Work orders and evidence flow into IBM Maximo, SAP Plant Maintenance, Infor EAM, Oracle eAM, UpKeep and Fiix so predictions become planned interventions.

"Predict the failure. Plan the intervention. Close the work order."

Sensing Coverage

Multi-modality

Vibration, acoustic, thermal, infrared, fluid and process data.

Analytics Model

Physics + ML

OEM signatures, unsupervised and supervised models per asset class.

Maintenance Workflow

CMMS-integrated

Maximo, SAP PM, Infor EAM, Oracle eAM, UpKeep and Fiix.

Platform ecosystem

Multi-vendor by design — devices, gateways, cloud IoT platforms and protocols are matched to the estate, integration surface and operating model.

IBM Maximo APM
GE Predix / Proficy
Siemens Senseye
SAP PdMS
AVEVA APM
PTC ThingWorx Kepware
Augury · Petasense
SKF · Emerson AMS

Scope in detail

Vibration & Acoustic Analytics
Accelerometer and ultrasonic sensors on motors, pumps, fans and gearboxes with the applicable ISO 20816 part (or equipment-specific OEM guidance) as the baseline, and machine-learning models trained to flag bearing wear and imbalance.
Thermal & Infrared Monitoring
Fixed IR cameras and thermal spot sensors on switchgear, transformers, panels and cabling for early detection of hot spots and insulation degradation.
Oil, Fluid & Wear Sensors
Inline particle counters, moisture and viscosity sensors for hydraulics, gearboxes and coolant so fluid replacement moves from calendar-based to condition-based.
ML Anomaly Detection
Unsupervised anomaly detection and supervised failure-mode models trained per asset class, with precision and recall reported to the reliability team.
Remaining Useful Life (RUL)
Physics-informed and data-driven remaining-useful-life estimates per critical asset so maintenance windows are planned against real degradation rather than conservative OEM tables.
CMMS / EAM Integration
Automatic work-order creation in IBM Maximo, SAP Plant Maintenance, Infor EAM, Oracle eAM, UpKeep and Fiix, enriched with sensor evidence, recommended parts and estimated time-to-failure.
Reliability & FMEA Workflow
Failure-mode and effects analysis captured as living artefacts that feed back into detection thresholds and spare-parts stocking policy for continual improvement.
Reliability & Cost Dashboards
Avoided downtime, spare-parts consumption and maintenance-hour reporting per asset class, providing a live business case for the reliability programme.

Where connected estates break down

Interval-based servicing over- or under-runs
Critical rotating equipment is serviced on fixed intervals regardless of usage or condition, leading either to over-servicing or to failure between intervals.
Sensor pilots that never reach work orders
Condition-monitoring pilots produce dashboards but do not create work orders in the CMMS, so findings are lost and reliability engineers are not engaged.
Reliability engineering separated from data
FMEA, spares policy and maintenance strategy are managed in spreadsheets, disconnected from live sensor evidence and failure-mode analytics.
No shared business case for reliability spend
Avoided downtime, spare-part consumption and maintenance-hour savings are not measured per asset class, so investment cases are difficult to defend.

Engagement approach

Deliver predictive maintenance from asset ranking and sensing design through analytics, CMMS integration and continual improvement of models and FMEA artefacts.

Assess
Rank critical assets by downtime cost, review current sensing, failure history, FMEA artefacts, CMMS integration and reliability roles and responsibilities.
Design
Select sensing modalities per asset class; specify anomaly and failure-mode models; define CMMS integration, work-order enrichment and dashboard portfolio.
Integrate
Deploy sensors, gateways and analytics; integrate CMMS and EAM systems; and enable reliability engineers, planners and maintenance leads on new workflows.
Operate & Improve
Track model performance, feedback failure evidence into FMEA and stocking policy, expand coverage to new asset classes and refresh dashboards against outcomes.

Deliverables & artefacts

Sensor plan for vibration, acoustic, thermal and fluid
Failure-mode and criticality analysis per asset class
Anomaly, supervised and RUL model specifications
CMMS / EAM work-order integration design
Model retraining and MLOps operating cadence
ROI baseline versus calendar-based maintenance

End-to-End IT Integration and Support

From assessment and solution design through implementation, remediation, ongoing support, and lifecycle optimization.

Fix What's Broken — Remediation Sprints

Offline sensors, unreliable gateways, broken protocol integrations, duplicate or missing telemetry, dashboard drift, expired certificates, alarm floods, or an IoT / OT pilot that never scaled? Jiva Technologies runs fixed-scope remediation sprints across the device, network, platform and integration layers.

Sensor, meter and gateway health audit, firmware baselining and connectivity fixes
Protocol integration cleanup (BACnet, Modbus, OPC UA, MQTT, LoRaWAN, LTE-M)
Data-model, KPI, dashboard and alarm rationalisation with tag / battery lifecycle fixes

Request a remediation scope

Support & Maintenance — AMC-Backed

Keep IoT, OT and smart-building estates running after handover — device health, platform hygiene, certificate and firmware lifecycle and vendor coordination under a Jiva AMC.

L1 / L2 / L3 technical support for leading IoT, edge, BMS, SCADA and analytics platforms
Preventive maintenance, sensor calibration, firmware, configuration backup and licence renewal
Vendor escalation, periodic service reviews and documentation refresh

Explore AMC & support contracts