Predictive Maintenance Forum


26-28 April | Online | 8 Tracks | 5 Speakers

5 May | Hands-on Physical Workshop - Singapore


The event is free but registration is required.


Ready? Game On!

GMT +8  | Singapore time

Preventive today, Predictive tomorrow

Predictive maintenance reduces operational costs for organizations running and manufacturing expensive equipment, by predicting failures from sensor data. However, identifying and extracting useful information from sensor data is a process that often requires multiple iterations as well as a deep understanding of the machine and its operating conditions.

Join the forum to learn everything about Predictive Maintenance and make Factories Smarter together!
predictive maintenance


Learn and network together 

Dr Vineet Jacob Kuruvilla
Industry Manager MathWorks Inc.

Dr Vineet is the Aerospace and Defense Industry Manager at MathWorks. He also leads the strategy development and execution for the Predictive Maintenance segment in the AeroDef industry.

Ian M. Alferez
Application Engineering Manager TechSource Systems

Ian specializes in in the field of embedded system (embedded coder configuration), data analytics (Machine Learning) and technical computing with MATLAB/Simulink. 

Dr Seow Kok Huei
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.

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.  

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26 April 2022 | 10:00 - 10:10 (GMT+8)
Preventive today, Predictive tomorrow
26 April 2022 | 10:10 - 10:45 (GMT+8)
How Industry 4.0 is changing our way of developing..

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.

Dr Vineet Jacob Kuruvilla

Industry Manager

MathWorks Inc.

26 April 2022 | 10:45 - 11:30(GMT+8)
Introduction to Predictive Maintenance

Learn how predictive maintenance lets you estimate the optimum time to do maintenance by predicting time to failure of a machine. This way, you can minimize downtime and maximize equipment lifetime. In this technical forum, you’ll learn how predictive maintenance works and how it is different from other strategies such as reactive and preventive maintenance. We will also walk you through a workflow that will help you develop a predictive maintenance algorithm.

You’ll learn about condition indicators and how you can extract them from your data to discriminate between healthy and faulty states. Machine learning models are trained using the extracted condition indicators to classify different types of faults. We will also help you understand different estimator models, such as survival, similarity, and degradation, that are used to estimate the remaining useful life of a machine.



• Introduction, different strategies and workflow
• Feature Extraction for Identifying Condition Indicators
• Remaining Useful Life Estimation

Ian Alferez

Application Engineering Manager

TechSource Systems

26 April 2022 | 11:30 - 12:00 (GMT+8)
Demo Showcases

• 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

27 April 2022 | 10:00 - 10:30 (GMT+8)
Predictive Maintenance and AI Techniques
Deploying Artificial Intelligence on a PLC (Predictive maintenance algorithm)

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.


• 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.

Ian Alferez

Application Engineering Manager

TechSource Systems

27 April 2022 | 10:30 - 11:15 (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



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

Dr Seow Kok Huei

Application Engineer

TechSource Systems

27 April 2022 | 11:30 - 12:00 (GMT+8)
Demo Showcases
• Deep Learning Audio Classification - Air Compressor Fault Detection
28 April 2022 | 10:00 - 10:45 (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


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

28 April 2022 | 13:30 - 16:40 (GMT+8) | Online
Predictive Maintenance Hands-on Workshop
- Limited seats for 30 pax Only
05 May 2022 | 9:30 - 12:40 (GMT+8) | Singapore Office
Predictive Maintenance Hands-on Workshop (In-person)
- Limited seats for 15 pax Only

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
• 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




Online via Zoom
Physical Workshop @ Singapore Office will be conducted on 5th of May 

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