Product Lineup
From edge-based diagnostics on the factory floor to API integration into existing systems and exploratory analysis by data scientists. toorPIA technology delivered in the optimal form for your use case. Please contact us for Pricing & Licensing information.
vibeCheck Introduction Video — From recording to precursor detection
Typical targets: conveyors, gearbox low-speed stages, agitators and cooling tower fans
Three-step workflow: Field measurement (1–2 min, no explosion-proofing required) → Transfer to vibeCheck unit in the office → Automatic analysis and results
vibeCheck data management screen: centralized management of recording data, pipeline settings, and diagnostic results per equipment
vibeCheck
Even at low speed, the early signs show up in the sound.
Conveyors, the low-speed stage of a gearbox, agitators, cooling tower fans, rotary dryers —the slower a machine turns, the less conventional vibration diagnosis has to work with. vibeCheck does not chase a particular frequency peak. It judges byhow far a recording sits from a base map built during normal operation, so you do not need to know in advance which frequency the fault will appear at.
Why low-speed machinery is hard to diagnose
Conventional vibration diagnosis
- •The signal itself is small — Excitation grows with speed. Unbalance force is proportional to the square of rotational speed, and a rolling element striking a spall hits with less force as its passing velocity drops. Meanwhile the background noise from nearby pumps and motors stays exactly where it was, so a weak signal gets buried
- •Fault repetition falls to a few events per second — Defect pass frequencies scale with shaft speed. Frequency resolution is the inverse of window length, so telling apart lines 0.1 Hz apart needs a window of 10 seconds or more. But the longer the window, the worse the time resolution — and a millisecond-long impact is averaged away (the preprocessing tradeoff)
- •Speed variation smears the peak — When load changes or a belt or chain slips, shaft speed moves and the frequency you are looking for moves with it. The more you average, the wider the peak spreads and the deeper it sinks into the floor. You can track it with a tachometer, but that means fitting one to every asset you want to watch
- •Envelope analysis needs the answer up front — Besides the band to demodulate, you need the rolling element count and dimensions, the tooth counts, and the speed of each stage before you can even compute which frequencies to look for. On multi-stage gearboxes, conveyor drivetrains and agitators, that information often simply is not on file
- •RMS velocity criteria have a lower speed bound — The evaluation zones in ISO 10816 / ISO 20816 are defined for machines down to roughly 120 r/min. Below that, the tables do not apply as written. Velocity is displacement times frequency, so the same defect reads as a smaller number the slower the machine turns
How vibeCheck looks at it
- •It listens to the ring, not the slow repetition — The defect may only come around a few times per second, but every single impact excites the structure and shows up as sound and vibration reaching far higher frequencies. That band is where vibeCheck looks for change
- •The whole spectrum is treated as one shape — The frequency spectrum from STFT is kept as a high-dimensional vector and projected into 2D by toorPIA. Nobody has to nominate which peak matters
- •Judgment is distance from a normal-state base map — Inside or outside the normal region, and whether the distance from its centroid exceeds twice the reference radius Rg. Those two checks give NORMAL / WARNING / DANGER. No prior knowledge of which frequency the fault occupies is required
What it tells you, and what it does not
What you get is “this is not how it usually sounds”. It does not name the failing component. Diagnosis and the decision to act stay with your engineers — vibeCheck lets them start that work earlier. It requires data from normal operation, and recordings taken from a consistent position with a consistent technique.
Can an anomaly be placed outside the normal region without knowing what kind of anomaly it is? We tested that property on a public benchmark using synthetic data with known ground truth — build the map from normal data only, then inject unseen anomalies, the same operation as precursor monitoringsee the validation→
Slow-turning equipment often sits where permanent sensors are hardest to justify — outdoors, in dust, or right next to moving parts. vibeCheck starts with nothing more than capturing sound during a routine inspection round.
No fixed sensors needed
Ideal for outdoor cooling tower fans, dusty conveyors and other locations where wiring work or equipment downtime make permanent sensor installation impractical. Just capture sound during daily inspections
Record with what you have
Record with your iPhone's built-in mic or wired/wireless mics. Off-the-shelf piezo pickups are also supported. Use the equipment you already have
Perfect for PoC
Validate the effect at small scale without expensive upfront investment. Smoothly transition to full rollout with a low-risk, staged approach
A Raspberry Pi 5-based edge-processing diagnostic device. Recording hardware is flexible to suit your needs —iPhone's built-in mic, wired/wireless mics, off-the-shelf piezo pickups, or 32-bit float audio recorders. No specialized instruments needed. Start advanced precursor monitoring simply by capturing sound during daily operations.
DSP filtering and denoising, conversion into frequency spectra via STFT, projection by toorPIA, and comparison against the base map —every step runs on the device. No data transmission to the cloud is required.
Operation Overview
The vibeCheck unit is set up in an office or control room, not at the equipment site. At the field, operators capture sound as part of daily inspections using an iPhone, a piezo pickup with an audio recorder, or similar devices. Recorded data is imported into the vibeCheck unit in the office, and diagnostic results are reviewed on the dashboard. No permanent equipment needs to be installed at the field.
Operation Workflow
Initial setup takes 5 steps. After that, operations are automated to just 3 steps.
After setup, just 3 steps — Simply import audio data and assign it to equipment; the configured pipeline runs automatically and diagnostic results are displayed. Alerts notify you automatically when precursors are detected.
To see how this workflow takes root on the factory floor,see our deployment approach→
Key Features
- •Edge-complete — No cloud required. Ideal for security-conscious environments
- •Pipeline Comparison — Compare and evaluate different DSP/STFT settings to find optimal analysis conditions
- •Web Browser Operation — No dedicated software required. Intuitive dashboard UI
- •Remote Maintenance — Secure remote access via Soracom connectivity
toorPIA API
toorPIA's dimensionality reduction engine and precursor monitoring system delivered as a REST API. By integrating into your existing systems and data pipelines, you can monitor high-dimensional data for early warning signs that emerge in transitional regions.
From base map creation to new data projection and diagnostic score retrieval, everything is completed via API calls. Continuously monitors tens of thousands of map points in the background, with support for filtering by status and tags, and automated integration from external systems through API Key issuance.
Key Endpoints
- •Map Creation — Generate base maps from high-dimensional data. Automatic learning of normal regions
- •Data Addition & Projection — Project new data onto the base map and return 2D coordinates
- •Precursor Monitoring — Returns distance scores from the normal region and transition stages (NORMAL/WARNING/DANGER), quantifying precursory signs before anomalies develop
- •Map Management — Filtering by status and tags, dashboard integration
Technical Specifications
- •REST API / JSON format
- •Supports both batch processing and streaming
- •API Key authentication
- •Integrates with existing ETL/MLOps workflows
LLM Integration
The Python clienttoorpiaincludes a built-in MCP server, enabling LLMs such as Claude to directly perform dimensionality reduction and precursor monitoring via toorPIA.
- •Install the client and MCP server together with
pip install - •LLMs can interactively perform data analysis and precursor monitoring
For the evidence behind the engine's performance,see the open benchmark→
Create base map (submit high-dimensional data)
Project new data onto existing map
Retrieve diagnostic results
{
"map_id": "m-2026-0401-001",
"status": "warning",
"score": 0.73,
"position": { "x": 0.42, "y": -0.18 },
"distance_from_rg": 1.84,
"threshold_rg": 1.0,
"in_normal_area": false
}
Running toorPIA in JupyterLab and performing cluster analysis with MapInspector
System architecture: Web Browser → JupyterHub → toorPIA Engine (inside Docker container)
toorPIA Analysis Package
An on-premises analytics platform that lets you use toorPIA in a JupyterHub + JupyterLab environment. Installed in Docker containers on your private cloud, your in-house data scientists can access the JupyterLab environment through a browser and perform exploration and analysis of high-dimensional data with toorPIA.
JupyterHub handles user authentication and session management, with each user working in an independent JupyterLab environment. The toorPIA engine is invoked as analysis commands, and results are interactively visualized within the Notebook. Data never leaves your premises, ensuring secure operations.
- •On-Premises Deployment — Secure operations without data leaving your premises. Runs in Docker containers on your private cloud
- •JupyterHub Integration — Multi-user authentication and session management. Instant access from a browser
- •MapInspector — Select clusters on projected maps and instantly compare key attribute differences. Toggle between heatmap and scatter plot views
- •Interactive Analysis — Execute commands in the Notebook to explore map creation, cluster comparison, and attribute extraction in real time
For how it tells high-dimensional states apart,see the technology page→
Pricing & Licensing
We will propose the optimal plan based on your usage scale and deployment model.
Please feel free to contact us.
