Predictive Maintenance Forum

The industrial world is rapidly changing with the emergence of Industry 4.0, which encompasses the growing complexity of software and an ever-increasing amount of data.

The increasing code base on industrial systems is a challenge for classically trained engineers who rely on traditional methods for programming and testing. Also, sensors on modern equipment collect a significant amount of measured data, which needs to be analyzed to gain knowledge about product quality, energy consumption, machine health status and other economically relevant parameters. With the exponential increase in operational data, AI has become a valuable tool to automate the processing of these data and derive actionable insights, for example, for predictive maintenance.

These two trends will challenge engineers to become proficient in using new methods to emerge from the transformation as leaders in their areas and with new business models for their market.

• Triplex Oil Pump Demo 

• Jet Engine Demo-walk through the predictive maintenance workflow and identify conditieon indicators.

**Actual timing may differ on actual day of event

Predictive maintenance is increasingly being adopted, as it can reduce unplanned downtimes and maintenance costs when industrial equipment breaks. In this technical forum series, you will see how you can use simulation models of industrial systems along with Model-Based Design to cover the entire predictive maintenance workflow. The workflow spans from data acquisition and preprocessing to design and deployment of the predictive maintenance algorithm onto a PLC and as standalone executable or web application.

Highlights:

• Learn the fundamental aspects of predictive maintenance.

• Learn how physical modeling can help you generate synthetic failure data necessary for the development of your predictive maintenance algorithm.

• See how the Classification Learner app enables you to train and validate your condition monitoring algorithm.

• Learn how to automatically generate code from your machine learning model and test it on real-time hardware (e.g., on a B&R PLC).

• Learn how to build a model to predict the remaining useful life (RUL) of your system.

Predictive maintenance allows equipment operators and manufacturers to assess the condition of machines, diagnose faults, and estimate time to failure. Because machines are increasingly complex and generate large amounts of data, many engineers are exploring deep learning approaches to achieve the best predictive results.

• Anomaly detection of industrial equipment using vibration data

• Condition monitoring of an air compressor using audio data

Highlights:

For this session will run through battery modeling and parameterization, SOC and SOH estimation and lastly determination of its remaining useful life through predictive maintenance algorithm

Highlights:

You will be able to learn:

This workshop is suitable for anyone who are interested in learning about the workflow for developing PdM algorithms with MATLAB and Simulink.

In this hands-on workshop, you will be able to learn:

• Generating data from Simulink
• Data pre-processing and feature extraction 
• Implementing feature extraction 
• Predictive modeling 

*Terms and Conditions:

• Due to the limited availability of seats, early registration is strongly recommended to ensure your participation.
• Registration will be confirmed upon your receipt of the confirmation, on a first-come-first-serve basis.
• Should you wish to cancel your registration, please inform us in writing to events@techsource-asia.com
• TechSource reserves the right to reschedule or cancel the event due to unforeseen circumstances. Every effort, however, will be made to inform participants of the change.