Predicting Machine Failures Before They Happen: A Python-Powered Approach to Industrial Maintenance

[grimreapermanasvi]

Talk title

Predicting Machine Failures Before They Happen: A Python-Powered Approach to Industrial Maintenance

Short talk description

Predictive maintenance combines data science and IoT to prevent equipment failures before they occur. In this talk, I’ll demonstrate how machine learning models can analyse sensor data from industrial machinery to predict breakdowns and reduce downtime. The session walks through data preprocessing, model selection, and evaluation techniques using Python, along with practical challenges like data imbalance and noisy sensor readings. Attendees will gain insight into applying ML for real-world maintenance problems and how to design scalable, production-ready solutions.

Long talk description

Predictive maintenance has become a crucial application of AI and machine learning in Industry 4.0. Instead of following fixed maintenance schedules, predictive systems analyse real-time machine data to forecast potential failures and optimise maintenance planning.

In this talk, I’ll share how I built a predictive maintenance pipeline for large industrial machines using Python. We’ll start by understanding the dataset, which includes vibration, temperature, and operational cycle readings, and move on to data cleaning, feature engineering, and model building using algorithms like Random Forests and XGBoost.

I’ll explain how the model predicts machine health conditions, detects anomalies, and integrates with dashboards for actionable insights. We’ll also discuss challenges like handling missing data, avoiding overfitting, and maintaining model accuracy in real-world environments.

By the end of the session, attendees will have a clear understanding of:

• How to approach predictive maintenance problems

• How to use ML tools effectively for industrial IoT data

• How such systems can save costs and improve reliability in manufacturing

This session will be particularly useful for data enthusiasts, ML practitioners, and anyone curious about the intersection of Python, AI, and industrial engineering.

What format do you have in mind?

Talk (20-25 minutes + Q&A)

Talk outline / Agenda

• Introduction to Predictive Maintenance and Industry (5 mins)
• Data understanding and preprocessing (5 mins)
• Model development: classification and anomaly detection (10 mins)
• Results, challenges, and deployment insights (5 mins)
• Q&A (5 mins)

Key takeaways

• Understanding how predictive maintenance works in real-world industries
• Applying machine learning to time-series and sensor data
• Common pitfalls in industrial ML applications
• Best practices for scalable model deployment
• Practical understanding of Python-based predictive systems

What domain would you say your talk falls under?

Data Science and Machine Learning

Duration (including Q&A)

30 minutes (20 minutes talk + 10 minutes Q&A)

Prerequisites and preparation

• Basic knowledge of Python and ML libraries (Pandas, scikit-learn)
• Knowledge of basic sensors
• Curiosity about AI applications in industry

Resources and references

No response

Link to slides/demos (if available)

https://docs.google.com/presentation/d/13_U6N9KR1An3qNbFjC54ulB3Ur2-gty7/edit?usp=sharing&ouid=111114813762986629562&rtpof=true&sd=true

Twitter/X handle (optional)

No response

LinkedIn profile (optional)

https://www.linkedin.com/in/manasvi-sri

Profile picture URL (optional)

No response

Speaker bio

I’m Manasvi Srivastava, a B.Tech student specialising in Artificial Intelligence and Machine Learning. My work focuses on applying ML to solve real-world problems — from healthcare to industrial automation. I’ve developed multiple end-to-end ML pipelines and IoT-integrated projects such as Predictive Maintenance for Large Industry Machines and CAREआवास, a smart healthcare platform.
I’m passionate about making technical topics approachable and sharing learnings with the developer community.

Availability

I am available for the PyDelhi meet on 8 November 2025

Accessibility & special requirements

None

Speaker checklist

• I have read and understood the PyDelhi guidelines for submitting proposals and giving talks
• I will make my talk accessible to all attendees and will proactively ask for any accommodations or special requirements I might need
• I agree to share slides, code snippets, and other materials used during the talk with the community
• I will follow PyDelhi's Code of Conduct and maintain a welcoming, inclusive environment throughout my participation
• I understand that PyDelhi meetups are community-centric events focused on learning, knowledge sharing, and networking, and I will respect this ethos by not using this platform for self-promotion or hiring pitches during my presentation, unless explicitly invited to do so by means of a sponsorship or similar arrangement
• If the talk is recorded by the PyDelhi team, I grant permission to release the video on PyDelhi's YouTube channel under the CC-BY-4.0 license, or a different license of my choosing if I am specifying it in my proposal or with the materials I share

Additional comments

I’m a first-time PyDelhi speaker and would love to receive feedback or mentorship on improving the talk.

[rpsmaini]

Hi @grimreapermanasvi. I have reviewed your proposal, and it looks good I liked how you mentioned your agenda and topics in a very concise manner. Quick question during your session: will you be showing us walkthroughs/Demos, or will it be just slides? Because we, as a community, believe in a more hands-on approach that engages our audience. Since this is your first talk, I would highly recommend that you show us a live demo or an example related to your session.
Also, kindly send your slides; it doesn't have to be a finalized version, a draft version would also work for us. If you have any doubts, questions, or concerns, you can address them in the comments. We will assist you. All the best
@Daksh777

[grimreapermanasvi]

Hi @rpsmaini, Thank you so much for reviewing my proposal and for the kind feedback! 😊

Yes, I’ll definitely include a live demo in the session — I plan to walk the audience through a real predictive maintenance pipeline using Python. The demo will cover how sensor data is preprocessed, how the model predicts machine failures, and how results can be visualised for insights.

Here is a draft of the slide: https://docs.google.com/presentation/d/1R_MH-N6ECoBmY_Ek9fyMGUvRvioMKQlf/edit?usp=drive_link&ouid=111114813762986629562&rtpof=true&sd=true

I'll be adding more slides, but here is a basic idea of it

Really appreciate the guidance and support — I’m excited to make this session as interactive and hands-on as possible!

[rpsmaini]

Hi @grimreapermanasvi, I have reviewed your slides. I have some feedback to share:

1. I went through the content it looks decent. You have tried to cover whatever you have mentioned in the agenda.
2. The not so ok part was, the slides were overcrowded, and you tried to push everything into one slide, making it wordy. Try to keep it simple. Move content to another slide if it's getting to much crowded.
3. Keep font consistent and align text properly throughout your slides, making sure it does not overlap with anything else.
4. Try to add an intro slide telling about yourself, then (it's optional) at lastly add a slide where you put your socials, so people can connect with you.

[grimreapermanasvi]

Hi @rpsmaini @Animesh-Ghosh , Thank you for the feedback.
I have added a page with my intro and to connect with me
I have removed some slides and also added a new slide with an error screen it's for the story buildup.
Here is the proposed slides draft: https://docs.google.com/presentation/d/13_U6N9KR1An3qNbFjC54ulB3Ur2-gty7/edit?usp=sharing&ouid=111114813762986629562&rtpof=true&sd=true

[grimreapermanasvi]

Hi @Animesh-Ghosh @rpsmaini @Daksh777 Could you please review the slides?
Please let me know of any further changes.