Anomaly Detection Without Fault Data: How Luviner Enables Predictive Maintenance from Day One
Most predictive maintenance systems need examples of every failure mode. Luviner only needs your normal operation data — and detects 100% of anomalies.
The Problem with Traditional Fault Detection
Every predictive maintenance project starts with the same question: “Where are your failure examples?”
In reality, most factories don’t have them. Failures are rare (thankfully), expensive to reproduce, and often never recorded. Traditional supervised classification needs labeled examples of every fault type — bearing wear, misalignment, imbalance — before it can learn to detect them.
This creates a chicken-and-egg problem: you need fault data to build the system, but you need the system to capture fault data.
One-Class: Learn Normal, Detect Everything Else
Luviner’s anomaly detection takes a fundamentally different approach. Instead of learning what faults look like, the model learns what normal operation looks like. Anything that deviates from that learned pattern is flagged as an anomaly.
The practical implications are significant:
- No fault data needed — train entirely on normal operation recordings
- Detects unknown faults — even failure modes you’ve never seen before
- Deploy from day one — record a few hours of normal operation and you’re ready
- Gets smarter over time — combine with on-device adaptation for continuous improvement
Benchmark Results
On our industrial vibration benchmark:
- 100% detection rate — every anomaly was correctly flagged
- 27x score separation — normal samples scored 0.04, anomalies scored 1.10
- Zero false negatives — no missed faults in any test run
The model produces a continuous anomaly score, not just a binary yes/no. This means you can set your own sensitivity threshold — catch early-stage degradation or only alert on severe deviations.
Runs on MCU, in Real Time
The anomaly detection model exports to pure C and runs in streaming mode on any supported microcontroller. Your sensor reads data, feeds it to the model, and gets an anomaly score — all in real time, all on-device.
- No cloud connection required
- Continuous monitoring — not batch processing
- Fits alongside your classification model
How It Compares
Edge Impulse offers anomaly detection but requires their cloud platform and subscription. TensorFlow Lite Micro can run autoencoders but requires significant ML expertise to set up. Luviner is the only platform that offers one-class anomaly detection as a built-in feature with automatic C export and MCU deployment.
Get Started
Upload your normal operation data as a CSV. Select anomaly detection mode. Luviner trains the model and exports it to your target MCU — ready to flash.