Predictive Maintenance with Machine Learning

Project Details

This project focuses on predictive maintenance to anticipate and prevent hard drive failures in data centers. Utilizing the Backblaze Hard Drive dataset, it analyzes daily S.M.A.R.T. statistics such as temperature, read/write error rates, and spin retries to identify early indicators of failure. Machine learning models including LSTM networks and Autoencoders are developed to estimate remaining useful life, detect anomalies, and map them to failures using service logs. This proactive approach reduces unplanned downtime, avoids unnecessary repairs, optimizes spare parts inventory, and improves operational efficiency.

Technologies Used