VectoraLab Engine
A high-performance modular framework for real-time vibration analysis, statistical feature extraction, and interpretable anomaly detection built for Industry 4.0 environments.
The Industrial Blind Spot: Reactive Maintenance
Unexpected machinery downtime costs global manufacturing billions annually. Current monitoring infrastructures are either completely reactive or operate as complex, closed-source 'black boxes'.
VectoraLab breaks this paradigm by combining mathematical transparency with low-latency edge processing.
Engineered for Precision
Multi-Domain DSP
Extract features across Time, Frequency, and Envelope domains.
Edge-Ready Low Latency
Optimized for industrial gateways and edge devices.
Statistical Feature Engine
Compute up to 24 statistical indicators per signal frame.
ISO 10816 Compliance
Integrate vibration standards directly into scoring pipelines.
Modular System Architecture
VectoraLab divides signal monitoring into decoupled processing stages.
Physical Sensors
Continuous high‑frequency acceleration signals
Data Acquisition
High‑resolution analog‑to‑digital conversion
DSP Preprocessing
Butterworth filtering and noise reduction
Feature Extraction
RMS, Kurtosis and spectral indicators
Statistical Distance
Mahalanobis deviation detection
Health Score
Interpretable score between 0 and 100
Signal Processing Pipeline
Time-Domain Signal
Live acquisition of raw sensor data (Vibration/Acoustic).
FFT Analysis
Converting signals to frequency domain to identify primary components.
Spectrogram
Time-frequency analysis to monitor non-stationary signal behavior.
Trend Extraction
Advanced feature engineering to track health degradation over time.
AI Anomaly Scoring
Unsupervised machine learning for early failure detection.
Interactive Health Score Engine
Simulate machine degradation and see how our AI computes the health index in real-time.
AI Insight: The asset is operating within nominal parameters. No maintenance required.
Interpretable Health Score Model
VectoraLab computes a deterministic health score from 0–100.
The Health Score H(t) is computed using weighted exponential decay.
Explore Math SandboxFeature Extraction Model
VectoraLab extracts statistical features from vibration signals to characterize machine condition and detect early anomalies.
RMS
x_rms = √(1/N * Σ x_i²)Root Mean Square (RMS) captures the overall vibration energy level.
Kurtosis
K = [Σ(x_i - μ)⁴ / N] / σ⁴Kurtosis detects impulsive mechanical events such as bearing impacts.
Skewness
γ = [Σ(x_i - μ)³ / N] / σ³Skewness measures asymmetry in vibration signal distribution.
Statistical Deviation Model
Each feature deviation is normalized using baseline statistics.
Real-time features are compared with baseline mean (μ) and standard deviation (σ).
Health Score Interpretation
| Health Index | Condition Status | Technical Interpretation |
|---|---|---|
| 80 – 100 | Normal / Optimal | Machine operating under normal condition. |
| 50 – 80 | Warning / Alert | Moderate degradation detected. Maintenance recommended. |
| 0 – 50 | Critical / Danger | Severe anomaly detected. Immediate action required. |
Applied Industrial Engineering
Rotating Machinery
Monitoring turbines, pumps, motors and gearboxes.
Predictive Maintenance
Early warning triggers before catastrophic failures.
Industrial R&D
Research platform for signal processing engineers.
Intelligent Industrial Alert Bot
Receive real-time machine alerts directly in Telegram the moment an anomaly or critical condition is detected.
Instant Alerts
Alert when machine health drops below threshold
Daily Reports
Automatic daily health summary every morning
Multi-Market
Each team manages its own monitoring scope
Dedicated Webhook
A unique HTTPS endpoint per user
After registering in the bot, your unique token is issued automatically.
Free · 7-day trial · No installation needed