Listen to your machine data.
Modern manufacturing involves countless sensors capturing vibration, temperature, torque, and power data. However, this data often sits unused until a machine fails. We build the analytics layer that detects early warning signs of equipment failure before they cause downtime.
The missing translation layer
Equipment failure is rarely instantaneous. A bearing that fails often shows subtle signs of degradation months in advance: altered vibration patterns, increased power draw, or slight temperature rises.
Standard alarms miss these early indicators because they are threshold-based, triggering only when a parameter enters a critical danger zone. The gradual drift goes unnoticed.
Our solution bridges this gap by applying statistical analysis to identify these subtle drifts in your existing sensor data, turning raw noise into actionable maintenance alerts.
What we deliver
A custom system that analyzes your historical sensor data, establishes a baseline for normal operation for each specific machine, and alerts your team to deviations.
Seamless Data Ingestion
We connect directly to your existing data infrastructure (Historians, SCADA, OPC UA, MQTT, or CSV dumps). No new hardware or sensors are required.
Machine-Specific Baselines
We characterize each machine individually based on its own history, preventing the false alarms common with generic, manufacturer-based models.
Advanced Drift Detection
Our algorithms detect statistical changes in data distributions rather than simple threshold breaches, catching subtle anomalies early.
Actionable Maintenance Dashboard
A prioritized dashboard highlighting machines with detected drift, complete with confidence scores and direct links to the underlying sensor data for verification.
Project execution
- 01
Data viability review
We analyze a sample of your historical data to confirm it can support predictive modeling before any commitments are made.
- 02
Modeling and backtesting
We build models and test them against your historical failures to prove they would have provided advance warning.
- 03
Deployment and tuning
We deploy the system and work with your maintenance team to tune alert sensitivity, ensuring a low false-alarm rate.
- 04
Handover
The fully documented code is transferred to your infrastructure. We offer optional ongoing support without vendor lock-in.
Our expertise
Our technical knowledge comes from years of working with data, extracting weak signals from noisy, incomplete data streams and quantifying certainty—skills directly applicable to analyzing industrial sensor data.
We focus on the statistical modeling and rely on your team's deep understanding of the machinery and manufacturing processes to contextualize the findings.
Requirements
To successfully implement predictive maintenance, we require the following:
- Historical sensor data (ideally 12+ months). Live-only feeds cannot be modeled retroactively.
- A log of past equipment failures or unplanned downtime to validate our models.
- An estimate of the cost per hour of unplanned downtime to assess ROI.
- A brief consultation with your maintenance personnel.
If you lack historical data, we can assist in setting up proper data logging as a first step.
System limitations
This system detects anomalies but does not certify machine safety. It supplements, rather than replaces, your existing maintenance schedules.
It cannot predict sudden failures (e.g., a sheared bolt) that lack preceding data signatures.
The system requires an initial tuning period in a live environment to minimize false alarms.
Evaluate your equipment
Let us know which machine's failure causes the most disruption, and whether you have its sensor history. We will quickly assess if predictive maintenance is viable.