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PREDICTIVE MAINTENANCE

Revolutionizing manufacturing efficiency by reducing unplanned downtime and optimizing maintenance costs with AI-powered Predictive Maintenance

Key Figures from our use case deployment

0.63PRECISION :Proportion of true positives among all predicted positives
0.83RECALL: Proportion of true positives among all actual positives
0.71 F1 SCORE : 2 * (precision * recall) / (precision + recall)

Business Challenge

According to a study by Aberdeen Research, unplanned downtime can cost as much as $260,000 per hour, with 82% of companies experiencing at least one unplanned downtime event per year. Traditional preventive maintenance strategies often lead to inadequate maintenance, wasting resources and increasing operational costs. 

Poor maintenance practices can also reduce the overall lifespan of equipment, leading to higher replacement and capital expenditure costs. Furthermore, inadequate maintenance can result in inconsistent product quality and higher defect rates, negatively impacting customer satisfaction and brand reputation.

The benefits of using AI in Predictive Maintenance

ACCURACY OF TRUE POSITIVES DETECTED BY OUR MODEL
  • Precision (proportion of true positives among all predicted positives) was 0.63 for our model
  • Recall (proportion of true positives among all actual positives) was 0.83 for our model
  • F1 score was 0.71 – an F1 score above 0.5 is considered good

Our AI Models at Different Stages of Predictive Maintenance

DATA COLLECTION & PROCESSING

  • Data cleansing, pre-processing
  • Feature selection, extraction

ANOMALY/FAULT DETECTION

  • Support Vector Machines
  • Neural Networks
  • Random Forest

PREDICTING OUTAGE

  • Convolutional neural networks
  • Time series model
  • Reinforcement learning

PROGNOSTICS

  • Hidden Markov Models
  • Recurrent Neural Networks

RECOMMENDATION FOR OPTIMIZATION

  • Reinforcement Learning
  • Decision Trees
  • Bayesian Networks


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If you would like to know more or discuss our use cases in detail