2. How important is equipment prognostics
• 80+ Plants, several thousand rotating equipment, translating to several thousand potential
failures
– Better monitor thy equipment
• Since 2013 Calpine has been using a 3rd party Anomaly Detection framework based on NASA’s
ORCA algorithm
– Blind spots
– No Fault Detection or Remaining Useful to speak of
• Improving on existing methods through a combination of DL and classical ML algorithms
3. Anomaly detection vs Fault Classification
Unknown unknowns Unknown knowns
Anomaly Detection, 2018 PHM Society Annual Conference Tutoria, Neil Eklund
4. Anomaly detection in context
Maslow Pyramid of needs
Sensor Data Collection
Maintenance Labeling
Remaining
Useful Life
Anomaly
Detection
Fault
Detection
Prognostics
6. Framework overview
Design Build
Publish Model
architecture
Get
New Data
QC Data
(Prep Training Data)
Deploy Model
Read
Model
Retrieve / Receive
New Data
Score &
Alert / Store
Data Science Team
Analysts
9. Hands on Machine Learning with Scikit-Learn and TensorFlow
Anomaly Detection and Fault Disambiguation in Large Flight Data:
A Multi-modal Deep Auto-encoder Approach
Kishore K. Reddy, Soumalya Sarkar, Vivek Venugopalan, Michael Giering
Denoised Stacked Autoencoders
Reconstruction
Error
Why Autoencoders:
- Semi Supervised
- Easy to train
- Can use on multi
modal and
heterogeneous
without feature
engineering
10. Isolation Forest for one class classification(OCC)
Isolation
Forest
Reconstruction
Error