Load a week of machine sensor CSVs, resample them onto one time base, and baseline every tag. Drift and step-change detectors flag the sensors that moved, and the pipeline delivers a live dashboard plus a ranked maintenance shortlist.
A pressure transducer rarely fails outright. It reads one percent high on Tuesday, two by Thursday, and by the time scrap shows up the trend is three weeks old. Raw-value alarms miss it because every threshold was set for a healthy sensor, and eyeballing forty tags in a historian client is nobody's idea of a Friday afternoon.
The whole point of this pipeline is that the detectors are tunable in real time. Thresholds live on sliders, not in a config cell you re-run; when maintenance argues a flag is too sensitive, you drag it and the shortlist updates while they watch. Every step previews as you build, so a bad timestamp column or a unit mix-up surfaces at load time, not after an hour of plotting. And because intermediates are cached, next week's folder recomputes in seconds instead of re-crunching the whole history.
A folder of per-day, per-machine CSV exports with a timestamp column and one column per tag. No live historian connection required — this works from the flat files your PLC vendor already dumps to a share. Twenty minutes gets you from folder to shortlist.
Drag the whole logs folder — one CSV per machine per day — onto the canvas. A single @load node batches all 35 files, unions their columns, and previews the merged table. Timestamps parse automatically, even with mixed formats across machines.
Add an @shape resample step and set a 1-minute grid with mean aggregation. Jittery timestamps from different PLCs snap onto one shared time base so every tag lines up. The preview chart redraws the moment you change the interval.
Chain a baseline step that takes each tag's Monday median as its reference and rescales the week to percent-of-baseline. Sensors with different units and ranges become directly comparable. Flat lines mean healthy; anything sloping is a suspect.
Add the drift detector and set two thresholds with sliders — slope per day for slow drift, jump size for step changes. Flags appear on the chart the moment a tag crosses either one. Tighten a slider and watch borderline sensors flip in and out of the flagged list.
Wire an @deliver dashboard showing every flagged tag with its drift score, plus a CSV export of the maintenance shortlist ranked by severity. Next week, drop the new folder on the same canvas — cached intermediates mean only the new files recompute.
Batch-load daily machine sensor CSVs into one merged, time-parsed table.
Resample to one grid, baseline each tag, and flag drift and step changes with tunable thresholds.
Publish a live drift dashboard and export a maintenance shortlist ranked by severity.
Align CMM measurement points to a nominal CAD mesh, compute point-to-surface deviation, and export a color-mapped glTF model with a pass/fail report.
Model post-closure pit-lake rebound in LSD — krige borehole heads against a NetCDF recharge grid and deliver a live 3D drawdown model plus report.
Validate 63 LAS drillhole logs against collar surveys, krige a lithology block model, and export one glTF the whole team can open.
Build churn features from Postgres events and Parquet snapshots, catch a leaky window visually, and score the holdout with ONNX — on one canvas.
Score a million-row Parquet batch with an ONNX model — training-identical normalization, live previews, scored Parquet and metrics out.
Turn raw LAS/LAZ sweeps from a mobile robot into a nav-ready occupancy grid and 3D map — registration to export with zero scripts.
Sync CSV sensor logs with live REST telemetry, flag outliers and dropouts, and score every robot pass/fail before the field trial.
Turn folders of line-scan images and a label CSV into an ONNX defect-segmentation pipeline with overlay QA and a per-coil defect report.
Turn GeoTIFF orthomosaics into a stitched land-cover raster with per-class area stats, using tiled ONNX segmentation you can preview at every step.
Join a live meter feed to 85 campus building meshes, aggregate it hourly, and paint a 3D heat-map that repaints as energy use shifts, plus a weekly report.
Calibrate live strain telemetry, place gauges on the bridge mesh from a CSV, and color the structure by stress live — plus an alert log on every exceedance.
Turn a CT DICOM series into an ONNX organ segmentation you QA in 3D, then export an annotated volume and a per-organ volumetrics report.
Drag the whole logs folder — one CSV per machine per day — onto the canvas. A single @load node batches all 35 files, unions their columns, and previews the merged table. Timestamps parse automatically, even with mixed formats across machines.
Chain a baseline step that takes each tag's Monday median as its reference and rescales the week to percent-of-baseline. Sensors with different units and ranges become directly comparable. Flat lines mean healthy; anything sloping is a suspect.
Add the drift detector and set two thresholds with sliders — slope per day for slow drift, jump size for step changes. Flags appear on the chart the moment a tag crosses either one. Tighten a slider and watch borderline sensors flip in and out of the flagged list.
Wire an @deliver dashboard showing every flagged tag with its drift score, plus a CSV export of the maintenance shortlist ranked by severity. Next week, drop the new folder on the same canvas — cached intermediates mean only the new files recompute.