Tech stack

Programming languages Machine Learning Libraries, Data Visualization & Statistical tools

Challenges

● Costly Downtime: Repeated failures of critical assets, like pumps, caused costly downtime, impacting production output and the company's bottom line. ● Inaccurate Anomaly Detection: Existing sensor data analysis methods, despite utilizing 52 sensors, lacked the accuracy needed for early identification of anomalies. ● Data Quality Issues: The provided labeled data, containing null and duplicate values, hindered accurate analysis.

Solutions

● Data Preparation: Conducted exploratory data analysis and applied statistical techniques to ensure data accuracy and reduce dimensionality. ● Anomaly Detection: Developed and implemented multiple machine learning models (IQR, K-Means Clustering, Isolation Forest) for accurate identification of anomalies in sensor data. ● Proactive Maintenance: Enabled proactive maintenance strategies based on early anomaly detection, reducing downtime and optimizing operational efficiency.

Business Impact

• Cost saving
• Data-Driven Decision-Making
• Improved operational efficiency
• Improving production output