GEM Precare Case Study: Predictive Analytics

How did GEMBO Precare enable increased uptime and improved productivity by accurate predictions?

Key Benefits

End to end generic workflow for failure prediction
Increase of of uptime and OEE availability for by 10s of Percentage via predictive maintenance
Reduction of Operating Expense (Opex) on maintenance personnel
Reduction of Capital expense (Capex) on parts
Increased Productivity

About the client

The customer is a multi $B Tier one manufacturer, with multiple factories around the globe. Manufacturing lines vary between electronic manufacturing to semiconductor, serving a variety of markets including Automotive, Networking, Industrial, Audio and Gaming.

Problem statement

Due to an elevated downtime at the back end test floor, with multiple of their handles, the customers reached out to GEM, asking for assistance. After analyzing the data (part of GEM Data Analyst Services) GEM identified a few top failures, impacting downtime the most. For example, a top failure event in their “S” (abstracting the vendor name) Handlers had been related to device drop error. The Pick up arm lifts the device but before it puts the socket into the shuttle it drops the device. If this happens, the operator stops the machine and calls the supervisor and if there is a device in the socket, he calls the maintenance who will remove the hardware and the operator redoes the complete lot. This type of failure drastically impacts productivity: impacts the OEE Availability with the resulting unplanned downtime and OEE Quality which is inaccurate since the rerun is not based on a defective device.

The customer expected to have a trending, modeling and predictions. Together with GEM, customer intends to establish a threshold which will trigger tickets with corrective actions for the maintenance team. The team should see to it that the trend will improve over time.

The solution

The client learned about the GEM Precare Cloud solution, which provides real-time ML and AI-based insights into the equipment's overall efficiency so that corrective steps can be made to improve overall equipment effectiveness and service quality. The solution incorporates connection and big data analytics into Industry 4.0 digital twins of equipment, such as turbines, generators, engines, and infrastructure in general, to improve overall equipment efficiency via real-time and predictive analytics.

To allow smooth deployment of the Data product into Precare ML/ AI workflow, GEM performed initial data analysis, around time and frequency of the failure across machines, verified results with the customer subject matter expert and execute a complete process:

Extract Trend
Split trend into train-test
Model Training on train data
Create Result Data/Predictions (2 weeks into the future)
Model Evaluation
Model Selection (best one)

Deployment of solution

Based on the evaluation of the performance of various prediction models GEM selected a model and deployed into its AI ML framework to the following components:

Precare Cloud (Cloud Edition): To perform machine data aggregation and storage, training and validation of machine learning models for machine vision and predictive maintenance, computation of KPIs, and display of data on dashboards
Precare Edge (Cloud Edition): To perform complex edge processing of machine digital twin data in real-time for latency-sensitive tasks, such as status and alarm notifications, as well as machine vision
AI/ML and Predictive Analytics Packages/ Data product: To provide increased equipment productivity through just-in-time maintenance scheduling

Key Results

Based on the evaluation of the performance of various prediction models GEM selected a model and deployed into its AI ML framework.

GEM successfully showed that the accuracy or the gap between the predicted and actual data is over 96%, which means that the customer can now accurately predict a future failure.

GEM and the customer are now working to deploy this on GEM out of the box Predictive analytics workflow allowing not only predicting future failure gradient increase, but also enabling a ticket to be automatically raised against the machine owner, triggering predictive maintenance job on the machines for this type of failure.

Conclusion

Equipped with Unique Artificial Intelligence and Machine learning, GEM Precare SaaS Predictive Analytics Industrial IOT Platform Is stronger than ever. GEM real-time & Predictive analytics are top of the lines in the Semiconductor, Electronic Manufacturing, and Automotive. GEM enables any customer user, from executive to machine operator, to get critical operational analytics insight, allowing them to take predictive actions. GEM Agents are able to take full advantage of the hardware they run on for complex event processing at the edge rather than in the cloud. It allows real-time remote monitoring and visualization via the cloud of critical parameters and signals with notifications for alarms and other events. This significantly helps manufacturing organizations reduce latency for time-critical monitoring and control applications.

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By gal.garniek • December 12, 2024

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