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![MPAM Logo_3400.png](https://static.wixstatic.com/media/6beda4_e86a935faa524b8e8c00594953eb181e~mv2.png/v1/fill/w_309,h_88,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/MPAM%20Logo_3400.png)
Machine Learning and Predictive Maintenance
![Plant5.jpg](https://static.wixstatic.com/media/6beda4_a1a65b7680fa475abc9a578796f80204~mv2.jpg/v1/fill/w_1098,h_732,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/Plant5.jpg)
![Predict1.gif](https://static.wixstatic.com/media/6beda4_1e962939811e4ebbbce6e8b042c2bc6e~mv2.gif)
![Predict2.gif](https://static.wixstatic.com/media/6beda4_26727bdfbbb44efa9c1662bcb3db9a8e~mv2.gif)
Using Machine Learning models and statistical testing we develop anomaly detection and prediction system that could:
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Enable practical application of Predictive Maintenance and reduction of spare part stocking
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Improve reliability by reducing downtime, and associated costs from forced outage through preemptive repair work
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