JolokLabsEngineering & Computing
Sensor Time Series and Condition Monitoring
Analyze vibration and sensor histories before training complex machine learning models. Master timestamp alignment, irregular sampling, statistical feature extraction, spectral FFT analysis, and anomaly alerting.
Syllabus & Learning Objectives
Each module follows the JolokLabs methodology: physical question first, numerical method second, and reproducible error check third.
Module 1
Inspecting Sensor Histories (Timestamps, units, sample rate distribution, and gaps)
Module 2
Resampling and Alignment (Handling asynchronous sensors and missing telemetry)
Module 3
Time-Domain Feature Extraction (RMS, crest factor, kurtosis, and rolling statistics)
Module 4
Frequency-Domain Analysis (FFT, spectral energy, dominant frequencies, and leakage)
Module 5
Baseline Health Indices (Statistical process control, Mahalanobis distance, thresholding)
Module 6
Capstone: Condition Monitoring Pipeline (End-to-end signal audit and false-alarm analysis)
Why JolokLabs Courses Are Different
Physical Intuition First
Start every engineering problem with governing mechanics, units, and boundary conditions before opening code.
Reproducible Code & Data
Real datasets (e.g. NASA C-MAPSS, ArduPilot SITL) without data leakage, synthetic shortcuts, or arbitrary assumptions.
Human-Audited Verification
Master error diagnosis, residual validation, and defensible engineering reports you can explain to a committee or manager.