Predictive Maintenance in Manufacturing

Challenge: The client produces industrial-sized equipment, relying heavily on an auto-lathe that utilizes a high-cost cam ($35,000) essential for operations. These cams, under significant manufacturing stress, were prone to unexpected failures, leading to extensive downtime of over 12 hours per incident, severely impacting production and operational costs.

‍

Solution:

‍

Technical Deployment:

Feuji introduced a cutting-edge sensor system designed to collect detailed acoustic data from the cams during operation. These sensors are strategically placed to detect the subtlest noises emitted by the cams, which are typically inaudible to human ears but indicative of mechanical health.

‍

Data Analysis and Feature Engineering:

The collected sounds are streamed in real-time to our AI processing unit, where advanced algorithms perform initial data cleansing and normalization. Using machine learning techniques, Feuji’s team engineered predictive features from this data, focusing on anomalies that correlate with the early stages of cam wear and tear.

‍

Predictive Modeling:

With the engineered features, we developed robust predictive models using machine learning algorithms capable of forecasting potential failures. These models are trained and tested on historical sensor data, fine-tuned to predict cam failures with a lead time of 48 to 72 hours before actual failure occurs.

‍

Implementation and Results:

The AI-driven predictive system was integrated into the client’s manufacturing process, with continuous monitoring and automated alerts set up to notify the maintenance team of the predicted cam failures. This proactive approach allows the maintenance team to plan and execute necessary repairs and replacements during scheduled downtimes, thereby avoiding unplanned production halts.

‍

Outcome:

The implementation of Feuji’s AI-driven predictive maintenance system transformed the client's maintenance strategy from reactive to proactive, significantly reducing unplanned downtime. The ability to predict cam failures well in advance resulted in smoother production flows, reduced maintenance costs, and enhanced equipment longevity.

‍