Most teams building failure-prediction for IoT hardware start with the wrong assumption: that more sensor channels = better predictions. In practice, the opposite is usually true.

A few things we've learned building this at PulseGuard, embedding failure prediction into other companies' hardware:

1. Vibration + temperature alone beats a 12-sensor stack in most mechanical failure modes. Adding channels without addressing sampling rate and noise floor just adds dimensions for your model to overfit to. Start with the two signals that correlate with the physical failure mechanism, not with everything the BOM happened to include.

2. Labeled failure data is almost always too sparse to train on directly. Most device fleets see failure rates under 1%. If you're building a classifier on raw failure labels, you're building on noise. Anomaly-scoring against a learned "healthy" baseline, then calibrating a threshold against the handful of real failures you do have, generalizes far better than a supervised approach on small fleets.

3. The alert has to live where the customer already looks, or it gets ignored. A dashboard nobody opens is not a maintenance program. If you're embedding prediction into someone else's product (device manufacturers, OEMs), the API/webhook has to slot into their existing support or fleet-ops tooling — not ask their customers to learn a new one.

If you're working on this problem — embedding predictive maintenance into connected hardware — happy to compare notes.