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Challenges

  • COVID-19 pandemic amplified the need for reliable cold storage for vaccine protection
  • A cold storage freezer’s failure can lead to the loss of valuable research and assets
  • Existing practices were obsolete
  • The need was to get rid of the waiting period to fix a failure
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Objective

  • To research & implement state-of-the-art AI/ML solutions & classify Normal Refrigerator Cycles from Refrigerator Short Cycles
  • To detect the number of Refrigerator Short Cycles behavior per day

Solution

AN ANALYSIS OF TIME-SERIES DATA

The subsequent solution pipeline is based on research & initial data visualization on Tableau
01
Exploratory Data Analysis
  • Convert time series data from text/JSON to CSV format, making a data frame through pandas
  • Analysis of every feature received in the data frame
02
Feature Engineering & Extraction Process
  • Check skewness in the data 12% for a short cycle 88% for a normal cycle
  • Remove unwanted refrigerator types, feature selection based on feature importance histogram analysis
  • Used clustering techniques to differentiate short cycles vs normal cycles and make labels

OBSERVATION

Clustering techniques could not differentiate between short and normal cycles
03
Feature Engineering - Further Analysis
  • Manuel labeling of each unique refrigerator type (mac_ids)
  • Converted original time-series data patterns into binarized data-patterns
  • Created 30 new features
04
ML Model Training And Validation
  • An unsupervised approach
  • clusters were developed based before labelling
  • Implemented manual clustering & advanced clustering techniques
  • Split data into Training,Validation, and Test Set
  • Trained model on state-of-the-art Ensemble ML Algorithm
  • Model evaluation analysis and hypert-uned model parameters

OBSERVATION

Short cycles & normal cycles weren't identified in 2 separate clusters. One cluster capturing bad data was removed as post-processing.
05
Current Model Performance and Deployment Strategies
  • Successfully able to classify refrigerator short cycles from normal cycles
  • Current Model Accuracy on Test Set: 97%
  • Ongoing improvements - Deciding probability score for short cycles before production, reducing False Positives and Negatives
  • Initial Model is deployed via Flask

Architecture Diagram

State-Of-The-Art ML Libraries & Packages

A Microservices Architecture

Result

Achieved a 20% increase in accuracy from the initial 65%

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