Most predictive maintenance models fail long before deployment — not because of the algorithm, but because of how the target is defined.
I worked on an industrial predictive maintenance problem involving air filter degradation and initially engineered pseudo-RUL labels using:
RUL = Maximum time − Time
The models looked excellent during validation.
Until I evaluated on the official run-to-failure test dataset.
R² collapsed to ≈ 0.21.
Feature importance analysis revealed the model was learning lifecycle progression more than actual degradation behavior.
That completely changed the direction of the project.
I redesigned the problem using a physics-informed target based on the actual failure threshold:
Target Capacity = 600 − Differential Pressure
That introduced another challenge:
target leakage.
After leakage-aware feature redesign and degradation-focused modeling, the refined model achieved:
• R² ≈ 0.94
• RMSE ≈ 30
More importantly, the model shifted toward learning degradation dynamics instead of reconstructing the target directly.
This project became less about model tuning and more about understanding what the model was actually learning.
Medium article:
medium.com/p/when-a-predi…
GitHub:
github.com/thatboypage/in…#MachineLearning#PredictiveMaintenance#DataScience#AI#Engineering
63’ GOAAAAALLLLL!!!!
DREAM DEBUT OLANREWAJU MOLADE!
The left back hits a direct free kick past the goalkeeper
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5 Years Course
6 Years Spent
67 courses done
38 A's
22 B's
5 C's
2 D's
0 E
0 F
(4.48 CGPA in Water Resources and Environmental Engineering)
Happy Convocation ALEYE UTHMAN (GMNSE). Alhamdulilah
Thank you @UnilorinNGR
Started from the streets in YABA,lagos
Happy he finally made his professional football debut.
He started chasing this dream since long ago. Way back from when when we were in SS3 , he wasn't able to any classes because he's in a football academy. Proud of you bro
@MoladeLanre