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

PRECISION :Proportion of true positives among all predicted positives

0.63

RECALL: Proportion of true positives among all actual positives

0.83

F1 SCORE : 2 * (precision * recall) / (precision + recall)

0.71

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

SCHEDULE A MEETING FOR PoC

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