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.

Download Full Case Study

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

Looking to Enhance Asset Reliability with our Anomaly Detection Solutions?

Talk to Our AI Expert!

Checkout Our Data Science & AI Solutions!

Enhancing Manufacturing Asset Reliability with Anomaly Detection

Industry

Company size

Headquarters

Tech stack

Challenges

Solutions

Business Impact

Looking to Enhance Asset Reliability with our Anomaly Detection Solutions?

Download Here

ADDRESS

PHONE

EMAIL