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
Speakers
Learn and network together
Nguyen Van Hung
Application Engineer Ascendas SystemsNguyen 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 SystemsKoh 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 SystemsClaudine 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 SystemsDr 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.
Agenda
- Tech Talk 1 Wednesday | 14 Sept 2022 (Closed)
- TECH TALK 2 Wednesday | 21 Sept 2022 (Closed)
- TECH TALK 3 Tuesday | 27 Sept 2022 (Closed)
- Hands-on Workshop Thursday | 6 Oct 2022
- Show All
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
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
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.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
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.