CASE STUDY
Enhancing Manufacturing Asset Reliability with Anomaly Detection
iLink Digital successfully helped the client to address the costly challenge of unplanned equipment downtime. By leveraging advanced anomaly detection through machine learning models and enabling proactive maintenance. This resulted in significant cost savings for the client.
Industry
Manufacturing
Company size
150K Employees
Headquarters
United States
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
