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Elevating Paper Production Efficiency: Predictive Maintenance with Machine Learning
Minimized downtime and optimized maintenance schedules.
Overview
As one of the largest paper producers in Brazil, our Accelerance partner leveraged Amazon Sagemaker and manufacturers to train a Deep Learning Model for predictive maintenance of paper machinery.
The goal was to empower engineers with data-driven decisions for maintenance, leading to increased operational efficiency, reduced downtime, cost savings, and an extended lifespan for critical equipment. Project timeframe was from April to July and a budget of 70K.
Industry
Manufacturing
Challenges
The paper production industry faces numerous challenges when it comes to machinery maintenance. Unplanned downtime can lead to significant losses in productivity and revenue.
Traditional maintenance approaches often struggle to accurately predict machinery failures, resulting in excessive manual inspections and unnecessary replacements.
Solutions
To address these challenges, they implemented a state-of-the-art Deep Learning Model using Amazon Sagemaker.
By analyzing historical data and identifying patterns, the model can accurately predict machinery failures before they occur.
This allows for proactive maintenance and efficient allocation of resources. The model continuously learns and improves over time, ensuring its predictions are always up-to-date.
Results
The implementation of the Deep Learning Model for predictive maintenance has yielded remarkable results at the paper producer.
Operational efficiency has significantly increased due to minimized downtime and optimized maintenance schedules.
This, in turn, has led to substantial cost savings for the company. Critical equipment now benefits from an extended lifespan, reducing the need for frequent replacements and resulting in long-term savings.
The engineers now have access to data-driven insights, empowering them to make informed decisions and take proactive measures to prevent machinery failures.