Predictive Maintenance in Manufacturing with TSFMs

Manufacturing floors generate enormous volumes of time series data. Vibration sensors on rotating equipment, thermocouples on furnaces, pressure transducers on hydraulic systems, acoustic emission sensors on weld joints, power meters on motors, and load cells on CNC spindles all produce continuous streams of readings at frequencies ranging from once per second to tens of kilohertz. Buried in these streams are the early signatures of equipment failure: a bearing that is beginning to pit, a cutting tool losing its edge, a compressor valve that is starting to leak. The challenge is detecting these signatures early enough to act. Time series foundation models are proving to be an effective tool for this problem.

The Maintenance Spectrum

Industrial maintenance strategies fall along a spectrum of sophistication and cost.

Reactive maintenance — run equipment until it breaks, then repair it — is the simplest approach and still common for non-critical assets. But unplanned downtime is expensive. A single unplanned stop on an automotive assembly line can cost $20,000 or more per minute when accounting for lost production, expedited parts, and overtime labor. Catastrophic failures also create safety hazards and can damage adjacent equipment.

Scheduled maintenance replaces components on fixed intervals regardless of condition. Bearings get swapped every 6,000 hours, cutting tools every 200 parts, filters every quarter. This eliminates some surprise failures but is inherently wasteful. Studies from the U.S. Department of Energy estimate that scheduled maintenance programs replace components with 30-40% of their useful life remaining, and they still miss the failures that do not follow predictable timelines.

Predictive maintenance monitors actual equipment condition and triggers intervention only when degradation is detected. It captures most of the reliability benefits of scheduled maintenance while reducing unnecessary part replacements and labor. The difficulty has always been building reliable degradation detection models, especially at scale.

Why TSFMs Fit Manufacturing

A typical manufacturing plant operates hundreds to thousands of distinct machine types: CNC mills, lathes, injection molding presses, conveyor drives, robotic welders, air compressors, chillers, and HVAC systems. Each machine type has different sensor configurations, operating profiles, and failure modes. Training a dedicated anomaly detection or remaining useful life (RUL) model for every machine type requires labeled failure data that most plants simply do not have.

This is where the zero-shot capability of TSFMs becomes a practical advantage. A foundation model pretrained on diverse time series data can detect deviations from expected sensor behavior on a machine it has never seen before. There is no need to collect months of run-to-failure data or label failure events for each equipment class. The model learns what normal temporal patterns look like — periodicity, trend stability, noise structure — and flags departures from those patterns. For a deeper look at when zero-shot inference is sufficient versus when fine-tuning is worth the effort, see our comparison guide.

Three Core Applications

Anomaly detection on sensor streams is the most immediate application. The forecast-residual approach works naturally on equipment sensor data: generate a rolling forecast of what the sensor reading should be, compare it to the actual reading, and flag significant deviations. A bearing entering early-stage degradation produces subtle increases in vibration amplitude at specific harmonic frequencies. A forecast-based detector catches the drift in overall vibration energy before the bearing reaches the damage threshold where audible noise or heat becomes apparent. Similarly, gradual compressor performance decay manifests as slow pressure loss or rising power draw that a TSFM can track against its expected trajectory.

Remaining useful life estimation extends anomaly detection into forecasting. Once a degradation trend is detected, a TSFM can project the sensor trajectory forward to estimate when a critical threshold will be crossed. The NASA Turbofan Engine Degradation dataset (C-MAPSS) is a widely used benchmark for this task: engines are run to failure while multivariate sensor streams are recorded, and models must predict cycles remaining before failure. TSFMs approach this as a forecasting problem on the degradation curve rather than requiring specialized RUL regression architectures. Additional manufacturing datasets for benchmarking are available through the PHM Society.

Failure mode classification distinguishes between different types of degradation. A CNC spindle might fail due to bearing wear, tool breakage, thermal expansion, or coolant system malfunction. Each failure mode produces a different signature in the sensor data. MOMENT, with its multi-task architecture, can classify failure types, detect anomalies, and impute missing sensor readings from a single pretrained backbone. This is particularly valuable when sensor dropout is common, as it often is in harsh factory environments where electrical noise, vibration, and temperature extremes can cause intermittent data loss.

Real Degradation Patterns

Certain failure signatures recur across manufacturing contexts.

Bearing degradation follows a well-studied progression. Initial pitting on the inner or outer race produces faint impulses at characteristic fault frequencies. As damage spreads, broadband vibration energy increases and temperature rises. The transition from detectable degradation to functional failure can take weeks or months on slow-rotating equipment, providing a wide intervention window if monitoring is in place.

Tool wear in CNC machining appears as gradually increasing cutting forces, rising spindle power consumption, and changes in acoustic emission signatures. A fresh cutting insert produces clean, periodic force patterns. As the edge wears, the force signal becomes noisier and the mean load drifts upward. TSFMs detect this drift in the force and power time series without needing wear-specific training data.

Compressor performance decay shows up as falling discharge pressure relative to suction pressure, increasing power draw for the same throughput, or rising discharge temperature. Valve leakage, ring wear, and fouling each produce subtly different trajectories in these sensor streams.

Edge Deployment Considerations

Many manufacturing environments cannot send sensor data to the cloud, whether due to latency requirements, network reliability, data sovereignty policies, or air-gapped security architectures. This makes on-premises and edge inference a practical necessity.

Smaller TSFMs are viable for CPU-based deployment on industrial PCs and edge gateways. IBM's Granite-TimeSeries (TTM) models, with parameter counts in the low millions, run inference on standard x86 CPUs with sub-second latency per sensor stream. Amazon's Chronos-Bolt-Small similarly targets efficient CPU inference while retaining strong zero-shot performance. These models do not match the accuracy of their larger GPU-bound counterparts on every benchmark, but for anomaly detection on individual sensor streams, their performance is more than sufficient — and the deployment simplicity is a significant operational advantage. Explore available model sizes and their tradeoffs on our model catalog.

Getting Started

A practical starting point is to select one critical machine class, collect a few weeks of normal operating data from its primary sensors, and run anomaly detection through TSFM.ai's API. No labeled failure data is needed for the initial deployment. As failure events are captured and labeled over time, they can inform threshold tuning and, if warranted, fine-tuning for improved detection sensitivity on specific equipment types.

The parallels to other industry applications are direct. Just as retailers use TSFMs to forecast demand across tens of thousands of SKUs without per-SKU models, and energy utilities forecast load across hundreds of grid zones, manufacturers can monitor thousands of machines with a single foundation model. The underlying principle is the same: pretrained temporal representations generalize across diverse time series, eliminating the per-target modeling bottleneck that has historically made predictive analytics impractical at scale. Try it in the playground or learn more about building production pipelines around TSFM inference.