Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Predictive Maintenance in HVAC industry

Predictive Maintenance in HVAC industry

730ee3af6c8be2f704bae5b17b27103d?s=128

Arnab Biswas

October 20, 2020
Tweet

Transcript

  1. Predictive Maintenance For HVAC Arnab Biswas Data Scientist, EcoEnergy, Carrier

    Global Corporation @arnabbiswas1
  2. HVAC (Heating, Ventilation, Air Conditioning) Controls temperature, humidity & air

    quality for indoor environment
  3. HVAC Equipment Accounts for 40% of energy usage in commercial

    buildings (US) Reduction in energy, cost & greenhouse gas emission can be achieved via proper design, installation & maintenance Heavily dependent on Annual Maintenance
  4. Types of Maintenance Type Part Replacement Part’s Life Utilization Downtime

    Reactive On Failure Complete Unplanned Proactive Periodic, Before Failure Pre-determined Scheduled Predictive Just Before Failure Almost complete Scheduled
  5. Predictive Maintenance Predict reason for failure Predict days to failure

    Predict a failure in future Predicting equipment’s failure over a future time period, based on its historical behavior
  6. PdM Architecture

  7. Data Science For PdM Understanding Business Problem Defining ML Problem

    Understand Data Prepare Data Build & Evaluate Model Deploy Model Maintain Model
  8. Business Problem into ML Problem • Predict if HVAC is

    going to fail within next 2 weeks Binary Classification • Predict amount of time before next failure Regression • Predict if HVAC is going to fail in next 3w, 2w, 1w Multi-class Classification • Predict if HVAC is going to fail within next 2 weeks for a particular reason Multi-class Classification
  9. Regression vs Classification? • Prefer Classification over Regression if business

    allows • Regression is equivalent to Classification with infinite number of classes • Single Stage Model • Multistage Model • Stage 1: Predict assets going to fail in next 2 weeks • Stage 2: Predict reason for failure for assets from Stage 1
  10. Data Temporal Health Return Air Temperature Supply Air Temperature Run

    Time Set Point Temperature Power Consumption Zone Temperature Weather Outside Air Temperature Relative Humidity Pressure Maintenance Failure History Repair History Root Cause Parts Replaced Others Building Occupancy Static Equipment Metadata Climate Zone Geographic Location Building Metadata Maintenance Agency Data Collection
  11. Prepare Data Structure data from various sources into tabular format

  12. • Standard: Forward Filling, Interpolation • Domain Specific: Fill missing

    value of vibration on 1 PM, Tuesday • with last Tuesday 1 PM’s value • with Tuesday 1 PM’s value averaged over last 1 month • Strategy should be validated using cross-validation Missing Value Handling (TS Data) • Maintenance Data needs attention Duplicate Handling • TS Data: Data with higher deviation from normal may indicate degradation • Maintenance data: May indicate human error Outlier Handling Data Preprocessing
  13. Feature Engineering (TS Data) • Rolling/Tumbling Aggregate • Need of

    Aggregation • Data Collection & Prediction frequency may not match • Data over individual time unit is noisy • Needs to be smoothened by aggregating over time windows • Lag Window • “How far in future the model has to predict” influences “how far in past the model has to look back” • Window Size should be validated using cross-validation Aggregation Average Maximum Minimum Median Standard Deviation Variance Count Sum Cumulative Sum Derivate 2nd Derivate Count of outliers
  14. Feature Engineering • Date • Day • Week • Weekday/Weekend

    • Month • Quarter • Year • … • Maintenance Data • Days since last failure • Days since last failure because of specific reason • Days since specific part replacement • Days since last maintenance • Static Data • Age of the equipment • Building Type
  15. • Cross Validation • Split Data in Time Dependent Manner

    • On Test Data • Split by Time • Separate Train & Test data by the window size • Split by Equipment • Better performance with new equipment • Metrics • Imbalanced Data • High Cost of False Positive Measure Model Performance
  16. Algorithms Phase I • Simpler Model • Simpler Features Phase

    II • Complex Features Phase III • Complex Models Multi-class Classification Regression RNN, LSTM RNN, LSTM DNN DNN GBM Random Forest SVM Hidden Markov Chain … GBM RF Regression …
  17. Model Serving & Monitoring • Serving : Batch Scoring •

    Monitoring • Compare predictions with Ground Truth (Maintenance Data from Future) • Degradation of model’s performance • Change in the incoming data • Bug in Data Pipeline • Drift in data
  18. References • Azure AI guide for predictive maintenance solutions •

    A process for implementing industrial predictive maintenance • A Survey of Predictive Maintenance: Systems, Purposes and Approaches • Rules of ML
  19. https://speakerdeck.com/arnabbiswas1/predictive-maintenance-in-hvac-industry @arnabbiswas1