{ "version": "2026-08-28", "scope": "Five offerings selected from the existing twelve-product predictive-maintenance catalogue, grouped into three documented data routes. This is a public-documentation comparison, not a measured vendor benchmark, a complete market survey or a deployment recommendation.", "products": [ { "slug": "siemens-senseye", "name": "Siemens Senseye", "route": "historian", "route_label": "Existing machine data", "input": "Time-series indicators and context; vibration waveforms or spectra.", "connection": "Historian, API, MQTT or S3/Azure Blob. Time-series ingestion is limited to 1 Hz per measure; higher-rate waveform ingestion is a separate path.", "output": "Condition indicators and degradation monitoring. A frequency spectrum and a slow trend are different inputs.", "check": "Submit the actual export format, sample rate and speed metadata. Do not downsample vibration waveforms as though they were slow process tags.", "sources": [ "senseye-data" ] }, { "slug": "aveva-pa", "name": "AVEVA Predictive Analytics", "route": "historian", "route_label": "Existing machine data", "input": "Operational time-series data; the current brochure documents native AVEVA PI System integration.", "connection": "PI integration and asset-model workflow. Confirm the actual historian/version and data-access licence in the quote.", "output": "Anomaly detection, fault diagnostics and time-to-failure forecasting are documented capabilities, not independently verified accuracy results.", "check": "Require a time-held-out test and per-fault forecast error. Do not transfer one customer's downtime savings to your fleet.", "sources": [ "aveva-product", "aveva-brochure" ] }, { "slug": "augury", "name": "Augury Machine Health", "route": "mounted", "route_label": "Machine-mounted sensing", "input": "Vendor IoT sensors measuring vibration, temperature and magnetic data; package depends on asset criticality and environment.", "connection": "Monitoring hardware plus analytics and reliability expertise. Public documentation describes CMMS/EAM and API integration routes.", "output": "Machine-condition diagnostics, severity and expert-validated guidance.", "check": "Match each asset's speed, mounting point and environment to the quoted hardware. Obtain the specific hazardous-area certificate where applicable.", "sources": [ "augury-health" ] }, { "slug": "tractian", "name": "Tractian Condition Monitoring", "route": "mounted", "route_label": "Machine-mounted sensing", "input": "Vibration, ultrasound, temperature and RPM in the condition-monitoring offering.", "connection": "Sensor-to-platform monitoring; documented integrations include SAP, Maximo and business-data systems. Connector scope still needs a quote.", "output": "Fault alerts, signal inspection and failure-to-work-order workflow.", "check": "Ask for waveform access, sampling settings and the exact hardware variant. Test how an alert becomes one work order, not several duplicates.", "sources": [ "tractian-monitor", "tractian-integrations" ] }, { "slug": "samotics", "name": "Samotics SAM4", "route": "cabinet", "route_label": "Electrical measurement at cabinet", "input": "Motor current and voltage measured at the motor control cabinet, with electrical-signature analysis.", "connection": "Cabinet hardware and cloud analytics. Standard external API metrics are at one-minute intervals; sub-second waveforms are not exposed through the standard API.", "output": "Validated fault findings, with REST/webhook and CMMS workflows. Monitoring does not issue process-control commands.", "check": "Confirm motor/drive/load suitability. Specify waveform access separately from dashboard/API metrics and verify the actual fault modes in scope.", "sources": [ "sam4", "sam4-integrations" ] } ], "sources": { "senseye-data": { "label": "Siemens machine-data interface", "url": "https://developer.siemens.com/senseye/machine/index.html", "anchors": [ "maximum frequency", "1Hz", "waveforms", "historian" ] }, "aveva-product": { "label": "AVEVA product capabilities", "url": "https://www.aveva.com/en/products/predictive-analytics/", "anchors": [ "Time to failure", "Anomaly detection", "Fault diagnostics" ] }, "aveva-brochure": { "label": "AVEVA current product brochure", "url": "https://www.aveva.com/content/dam/aveva/documents/perspectives/brochures/Brochure_PredictiveAnalytics.pdf.coredownload.inline.pdf", "anchors": [ "PI System", "integration" ] }, "augury-health": { "label": "Augury Machine Health and FAQ", "url": "https://www.augury.com/machine-health/", "anchors": [ "vibration, temperature", "magnetic", "CMMS", "APIs" ] }, "tractian-monitor": { "label": "Tractian condition monitoring", "url": "https://tractian.com/en/solutions/condition-monitoring", "anchors": [ "Ultrasound", "Temperature", "RPM", "work" ] }, "tractian-integrations": { "label": "Tractian integration catalogue", "url": "https://tractian.com/en/solutions/integrations", "anchors": [ "SAP", "Maximo" ] }, "sam4": { "label": "SAM4 system and installation scope", "url": "https://samotics.com/sam4", "anchors": [ "current and voltage", "cabinet", "cloud" ] }, "sam4-integrations": { "label": "SAM4 integration boundaries", "url": "https://samotics.com/integrations", "anchors": [ "1-minute", "Sub-second", "webhooks", "control commands" ] } }, "pilot_checks": [ { "name": "Agree the counting unit", "test": "Define one fault episode per asset and failure mode, the warning window, repeat-alert suppression and how an episode closes.", "evidence": "Versioned event-matching rules signed off before scoring. Keep ambiguous and unadjudicated episodes separate." }, { "name": "Separate training from evaluation", "test": "Choose the evaluation dates before model tuning. Keep the tested assets and operating regimes identifiable.", "evidence": "Training cutoff, model version, evaluation period and a log of any retuning. Do not leak later failure labels into training." }, { "name": "Include normal operating changes", "test": "Replay startup, shutdown, speed and load changes, plus maintenance interventions, alongside genuine fault episodes.", "evidence": "Regime-labelled event log and false alerts by regime, not just a single fleet-wide percentage." }, { "name": "Track coverage and missing signals", "test": "Remove a channel or interrupt a gateway in an approved offline test. Check for a data-quality warning rather than a silent healthy status.", "evidence": "Online asset-days, missing samples, sensor-health alerts and excluded periods with reasons. Never interrupt a live protection system." }, { "name": "Adjudicate alerts and missed faults", "test": "Match alerts to inspection findings and independently recorded fault episodes. Review both alerted and non-alerted assets.", "evidence": "True detections, false alerts and missed episodes under the same matching rule. Unresolved events remain unresolved, not automatically false." }, { "name": "Record actionable warning time", "test": "For each detected event, record first qualifying alert, inspection, intervention and failure time if actually observed.", "evidence": "Event-level timestamps. An intervention can prevent observing failure; do not invent the unobserved time-to-failure." }, { "name": "Follow one finding into maintenance", "test": "Replay one alert, then resend it, update severity and record the maintenance response in a sandbox CMMS.", "evidence": "Stable asset IDs, a single incident/work-order relationship, duplicate handling and the technician's finding." }, { "name": "Price and export the whole system", "test": "Request the hardware, gateways, analysts, licences, integration, retention and exit terms as separate quote lines.", "evidence": "Written scope plus sample raw-data/feature/event exports. A dashboard screenshot is not proof that waveforms are exportable." } ] }