Predictive Maintenance

Tech Talk Live 2022

14 - 27 Sept | Online | 4 Tracks | 3 Speakers


6 Oct | Hands-on Workshop - Online

( Limited Seats Offer)

 

The event is free but registration is required.

 

Ready? Let's Go!

GMT +8  | Singapore time

Embracing Predictive Maintenance Now for a Better Future

Predictive maintenance lowers operational costs for industries that run and manufacture expensive equipment by predicting faults based on sensor data. However, identifying and extracting usable information from sensor data is a process that frequently requires multiple iterations as well as a deep understanding of the machine and its operating conditions.

 
Join the Technical Talks and Hands-On Workshop to learn everything about Predictive Maintenance and obtain skills for constructing PDM Algorithms!
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Speakers

Learn and network together 

Nguyen Van Hung
Application Engineer Ascendas Systems

Nguyen Van Hung works for Ascendas Systems in Vietnam as an Application Engineer. He works with customers in Southeast Asia to introduce and support MATLAB and Simulink products.  He specialized in predictive maintenance, Polyspace, Automotive, and model-based design with MATLAB and Simulink.

Koh Jun Jie
Customer Success Engineer TechSource Systems

Koh Jun Jie works for TechSource Systems as a Customer Success Engineer. He has experience creating a UAV tricopter using MATLAB and Simulink, as well as being involved in projects involving machine learning, deep learning, predictive maintenance, image and signal processing.

Claudine Casipit Papag
Application Engineer TechSource Systems

Claudine C. Papag is an Application Engineer at TechSource Systems. She specialized in modeling and simulation, Verification, Code generation, and tool automation with MATLAB/Simulink/Stateflow and cross software configuration.  

Dr. Seow Kok Hui
Senior Application Engineer TechSource Systems

Dr Seow Kok Huei is a Senior Application Engineer at TechSource Systems, specializing in the application of data engineering, bioinformatics, Image Processing, Deep Learning, and Machine learning to biological datasets for diagnostics.

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Agenda

14 September 2022 | 3:00PM-4:00PM (GMT+8)
Predictive Maintenance with MATLAB

Do you work with operational equipment that collects sensor data? In this session, we will learn how you can utilize that data for Predictive Maintenance, the intelligent health monitoring of systems to avoid future equipment failure. Rather than following a traditional maintenance timeline, predictive maintenance schedules are determined by analytic algorithms and data from sensors. With predictive maintenance, organizations can identify issues before equipment fails, pinpoint the root cause of the failure, and schedule maintenance as soon as it's needed.

 

Highlights:

Accessing and preprocessing data from a variety of sources

Using machine learning to develop predictive models

Creating dashboards for visualizing and interacting with model results

Deploying predictive algorithms in production systems and embedded devices

Using simulation to generate data for expensive or hard-to-reproduce failures

Nguyen Van Hung

Application Engineer

Ascendas Systems

21 Sept 2022 | 3:00 PM - 4:00 PM (GMT+8)
Developing a Digital Twin for RUL Estimation A Battery Degradation Example

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:

• Model a battery using Physical Networks
• Update Battery Model Parameters (SOC/SOH) during Operation
• Data driven approach to estimate RUL

Claudine Casipit Papag

Application Engineer

TechSource Systems

27 Sept 2022 | 3:00PM- 4:00PM (GMT+8)
Deep Learning for Predictive Maintenance

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:

• Explore deep learning approaches to predictive maintenance by detecting anomalies and identifying faults in industrial equipment sensor data.

Koh Jun Jie

Customer Success Engineer

TechSource Systems

6 Oct 2022 | 3:00PM- 5:00PM (GMT+8)
Predictive Maintenance Hands-on Workshop - (Online)
Limited Seats

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.

 

**Actual timing may differ on actual day of event

Koh Jun Jie

Customer Success Engineer

TechSource Systems

Dr. Seow Kok Hui

Senior Application Engineer

TechSource Systems

Location

Online via ZOOM

Top Questions for Predictive Maintenance

Tell us your burning questions for Predictive Maintenance or MATLAB, we will answer you one-by-one!

Why are we interested in Predictive Maintenance?

Costs reduction, Performance Improvement, or do you want to provide a condition monitoring / predictive maintenance service to users of your product?  

 
...But Deploying a Predictive Maintenance Algorithm Successfully Is Much More Complicated

We hear you...

The Challenges Associated With Predictive Maintenance Are Consistent Across Industries, for both Data Scientists & Engineers

We will discuss more during the Forum........................

Developing A Predictive Maintenance Algorithm Requires Domain Expertise and Machine Learning Techniques...

We hear you... Let's discuss more
- Explore and automate feature extraction & machine learning tasks using MATLAB Apps

Visualize Data, Try Different Feature Extraction Methods & Compare Results Without Writing Any MATLAB Code........................

How are MathWorks Tools Used for Predictive Maintenance?

We will show you Who, How, When through case studies, demos, etc.  And if you are lucky enough to get a seat for our hands-on workshop, experience yourself and learn more.

 

Register Online