Subsystem 01
Telemetry ingestion
Vibration, temperature and acoustic sensor streams
Typical stack
MQTT + Apache Kafka
Reference architecture
Industrial IoT predictive maintenance MLOps platform
An MLOps platform that flags likely machinery failures early enough to schedule repairs, instead of finding them as unplanned downtime.
Design constraints
What the design has to hold to
Targets for the scenario this reference is sized for. A real engagement starts by replacing them with your own numbers.
- 01Flag bearing and motor anomalies with enough lead time to plan repairs (target: 48 hours)
- 02Process 50,000 sensor readings per second with automated feature computation
- 03Automated model evaluation and retraining when drift is detected
- 04Edge inference that keeps working in plants with intermittent or no connectivity
Component topology
System components and technologies
Subsystems with separate responsibilities, clear contracts between them and storage that scales on its own. The stack named for each is typical, not mandatory.
Stack topology
Industrial IoT predictive maintenance MLOps platform
Illustrative reference architecture
- 01
Telemetry ingestion
Vibration, temperature and acoustic sensor streams
MQTT + Apache Kafka
- 02
Feature store
Historical and real-time sensor features
Feast + Redis / Snowflake
- 03
ML training & experimentation
Time-series anomaly detection and remaining useful life (RUL) models
PyTorch + MLflow
- 04
Edge inference engine
Local model evaluation on factory hardware
ONNX Runtime + NVIDIA Jetson
Subsystem 02
Feature store
Historical and real-time sensor features
Typical stack
Feast + Redis / Snowflake
Subsystem 03
ML training & experimentation
Time-series anomaly detection and remaining useful life (RUL) models
Typical stack
PyTorch + MLflow
Subsystem 04
Edge inference engine
Local model evaluation on factory hardware
Typical stack
ONNX Runtime + NVIDIA Jetson
Data lifecycle
End-to-end data flow
Vibration sensors stream 1 kHz readings to edge gateways on the factory floor.
Each gateway computes FFT frequency features and runs ONNX inference every 5 seconds.
Features and anomalies sync to central Kafka topics in the cloud.
Model performance is tracked in MLflow, and detected concept drift triggers a retraining pipeline.
Maintenance managers receive work order recommendations on a mobile dashboard.
Reliability and resilience
Failure modes and how each is contained
Failure mode 01
Sensor drift causing false alarms
Mitigation
Baseline normalization that adapts to seasonal changes in ambient temperature.
Failure mode 02
Factory network disconnections
Mitigation
Gateways buffer telemetry on local NVMe disks and keep alerting on their own while offline.
Failure mode 03
Concept drift after an equipment overhaul
Mitigation
Retraining triggered by Kolmogorov–Smirnov tests on the feature distributions.
Questions
What teams ask about this design
Typically autoencoders for unsupervised anomaly detection, with LSTM or transformer models and XGBoost for remaining useful life (RUL) estimation. The choice depends on how much labeled failure history you have.
Yes. The edge inference stack runs on standalone containerized servers with no internet connection; updated models are brought in through the plant's approved transfer process.
Planning a system like this?
Send us your requirements, expected load and budget. We'll reply within one business day with an honest read on the design, and on whether we're the right team to build it.