A Deployable IIoT Framework for Condition-Based Maintenance: Case Study on LSTM RUL Prediction with NASA C-MAPSS
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Abstract
Condition-Based Maintenance (CBM) is basically the backbone of keeping industrial IoT (IIoT) setups running smoothly and steadily. Less downtime, more reliability—what’s not to love? Yeah, we’ve got all these slick machine learning (ML) and deep learning (DL) models for predicting Remaining Useful Life (RUL), but honestly, actually getting this stuff working out in the real world? That’s a whole other headache. There’s the mess of scaling, getting different systems to talk to each other, and wrangling real-time data—just to name a few. In this paper, we roll out a hands-on IIoT framework for CBM that ties together data collection, edge/fog processing, some solid ML, and cloud magic. We put it to the test with a case study using the NASA C-MAPSS dataset, where an LSTM model does some seriously impressive RUL predictions. Bottom line: this framework nails real-time monitoring and predictive maintenance in IIoT setups. It should not be taken as a theoretical assumption; rather, it is practical in fact.
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