Predictive maintenance gets pitched as a modeling problem, but in practice it's usually a data problem first. A sophisticated model trained on sparse, noisy, or mislabeled sensor data will underperform a simple model trained on well-instrumented, well-labeled data almost every time. Before evaluating algorithms, it's worth being honest about whether the underlying data actually supports the prediction being asked of it.
Sensor coverage has to match the failure modes you're predicting
The first question isn't "what algorithm should we use," it's "does our sensor placement actually capture signal related to the failures we care about." A vibration sensor mounted on the wrong part of a machine, a temperature sensor that measures ambient rather than component temperature, or a sampling rate too low to catch a fast transient event will all silently cap how good any model built on that data can be — no amount of modeling sophistication compensates for measuring the wrong thing.
Sampling rate and resolution
The right sampling rate depends entirely on the physics of the failure mode being predicted. A bearing failure that manifests as a high-frequency vibration signature needs sampling fast enough to resolve that frequency — a low-rate sensor will simply never see it. A slow thermal degradation process, by contrast, can be tracked with infrequent sampling and gains little from high-frequency data. Matching sensor sampling rate to the actual failure physics, rather than defaulting to whatever rate is convenient, is a foundational decision.
Labeled failure history is the hardest requirement to satisfy
Supervised predictive-maintenance models need examples of actual failures to learn from, and this is usually the scarcest resource in the whole pipeline — failures are, by definition, rare events, and historical failure records are often incomplete, inconsistently logged, or missing the sensor data from the period leading up to the failure. Products just entering the field have essentially no failure history yet, which shapes what's realistic: early deployments often start with anomaly-detection approaches that don't require labeled failures, transitioning to supervised failure prediction as real-world failure data accumulates.
Where labeled-data gaps commonly show up
- Maintenance logs recorded manually and inconsistently, disconnected from the sensor timestamp that would let them be correlated
- Failures that were caught and fixed before sensor data captured the full lead-up window
- Near-miss events (caught by a technician before failure) going unrecorded, losing valuable "about to fail" signal
- Different failure modes lumped together as a single generic "failure" label, losing information a model could otherwise use
Data quality problems that undermine an otherwise-good dataset
- Sensor drift going uncorrected over the device's field life, silently degrading data quality over time
- Missing data from connectivity gaps treated inconsistently (interpolated, dropped, or zero-filled) without a clear, consistent policy
- Environmental confounders — a temperature sensor reading affected by ambient conditions unrelated to the equipment's actual health — not accounted for in the model
- Inconsistent units or calibration across a fleet where hardware revisions changed sensor specs mid-deployment
Start with what the data can actually support
A realistic predictive-maintenance roadmap usually starts with anomaly detection (flagging when a device's behavior deviates from its own normal baseline, requiring no failure labels) and evolves toward full failure prediction as labeled data accumulates from the field. Committing to a fully supervised failure-prediction model before there's a meaningful failure dataset to train it on is a common way these projects stall.
How we approach this
We assess sensor coverage and data quality before committing to a modeling approach, and we're direct about when a product needs more field time before failure prediction is realistic. See our AI & Intelligence work for how this fits into building predictive-maintenance capability from hardware through model deployment.