Overview
Modern manufacturing equipment generates data continuously. CNC machines report spindle speed, feed rate, and tool load. Production lines report cycle times, reject counts, and line speed. Sensors measure temperature, pressure, vibration, and flow at every critical point in the process. PLCs and SCADA systems capture the state of every actuator and input across the facility. The data exists. The question is whether it is being collected, processed, and used — or whether it is being generated and discarded because nobody has built the infrastructure to make it useful.
The gap between the data that manufacturing equipment generates and the operational intelligence that management and engineers need is a processing problem. Raw machine data is high-frequency, high-volume, and structurally complex — arriving from many sources in formats that differ by equipment type, manufacturer, and protocol. Converting it into the OEE calculations, quality correlation analyses, predictive maintenance signals, and operational dashboards that drive manufacturing improvement requires data ingestion infrastructure, processing logic, storage architecture, and presentation layers that are built for the specific equipment, processes, and operational questions of each facility.
We build machine data processing systems for manufacturers, process industries, and industrial operations that need to move beyond manual data collection and periodic reports — capturing machine data in real time, processing it into the metrics that matter, and delivering it to the interfaces that operators, engineers, and managers use to run the facility.
What Machine Data Processing Covers
Data ingestion from industrial systems. Manufacturing facilities use a wide range of data sources and communication protocols. Machine data processing starts with connecting to whatever sources the facility runs.
OPC UA — the modern industrial communication standard for machine data exchange — provides a standardised interface for connecting to CNC machines, PLCs, SCADA systems, and industrial robots from any manufacturer that has implemented the standard. OPC UA connectivity provides real-time access to the data items that the machine exposes — the tags, variables, and events that represent the machine's operational state.
MQTT — the lightweight publish-subscribe messaging protocol widely used in IIoT deployments — connects sensors, edge devices, and industrial gateways that publish machine data to a message broker. MQTT connectivity subscribes to the relevant topics and processes the incoming messages as they arrive.
Modbus — the legacy serial and TCP communication protocol that remains common in older industrial equipment — provides access to the register values that PLCs and instruments expose over the Modbus protocol. Modbus polling at configurable intervals reads the current values from connected devices.
SCADA and historian integration — connecting to the SCADA systems and process historians (OSIsoft PI, Wonderware, Ignition) that already aggregate machine data in many facilities — avoids duplicating the data collection infrastructure that is already in place and focuses the processing on converting the data the historian holds into the operational intelligence the facility needs.
Database and file-based integration for production systems that export data through databases or files — the production log exported from the MES at shift end, the quality data table in the production database — pulls this data into the processing pipeline alongside real-time protocol data.
Data normalisation and context enrichment. Raw machine data is timestamps and values. Operational intelligence requires context — which machine, which product, which shift, which order, which operator. Data normalisation maps the raw data points from their source identifiers to the operational taxonomy that the facility uses: equipment hierarchy, work centre structure, product codes, order references.
Time synchronisation across data sources is critical for correlation analysis — understanding whether a vibration spike preceded a quality defect requires that both data points are timestamped to a common reference. Normalisation applies the time offsets and skew corrections that align data from sources with different clock references.
Unit conversion, scaling, and engineering unit application convert the raw values that machines report — ADC counts, raw sensor voltage, integer register values — into the engineering units that operational analysis requires: temperature in degrees Celsius, pressure in bar, speed in metres per minute.
OEE calculation. Overall Equipment Effectiveness is the primary productivity metric for manufacturing equipment — the product of availability, performance, and quality that quantifies how effectively the equipment is being used relative to its theoretical maximum. OEE calculation from machine data requires the availability data that downtime events provide, the performance data that actual cycle time versus theoretical cycle time provides, and the quality data that good parts versus total parts produced provides.
Automated OEE calculation from machine data replaces the manual data collection that paper-based OEE systems require — timesheets, downtime logs, production counts entered manually at shift end — with continuous, accurate calculation from the machine signals that record the actual events. Automated OEE surfaces the losses as they occur rather than in the shift-end report, giving production teams the real-time visibility to respond to performance losses before the shift is over.
OEE decomposition by loss category — planned downtime, unplanned downtime, minor stops, speed loss, startup rejects, process rejects — identifies the specific losses that drive the OEE gap and focuses improvement efforts on the categories that represent the largest opportunity.
Downtime analysis and classification. Machine downtime events — from the state change data that machine signals provide — are captured with their start time, duration, and where machine or operator classification is available, their cause code. Downtime analysis aggregates this event data into the Pareto of downtime causes that maintenance and engineering teams use to prioritise reliability improvement.
Automatic downtime detection — identifying when a machine transitions from running to stopped, and detecting the events that represent unplanned stops versus planned maintenance — removes the manual recording effort that accurate downtime data currently requires and eliminates the under-reporting that manual systems produce.
Quality correlation analysis. Manufacturing quality problems are often driven by process conditions — the temperature that drifted outside specification, the feed rate that was too high, the raw material batch with marginal properties. Correlating the quality outcome data — pass/fail results from in-line measurement, defect counts from end-of-line inspection — with the process data that machine data processing captures enables the root cause analysis that drives quality improvement.
Quality correlation analysis requires the data infrastructure to capture both the process data and the quality data with sufficient temporal resolution and accuracy to identify the correlations that manual analysis cannot find in the volume of data that continuous process monitoring generates.
Predictive maintenance signals. Machine condition signals — vibration spectra, temperature trends, power consumption patterns, cycle time drift — change in characteristic ways as equipment deteriorates. Processing these signals with the analysis logic that identifies deterioration patterns before they become failures provides the advance warning that predictive maintenance programs depend on.
Predictive maintenance signal processing requires the historical baseline data that establishes what normal looks like for each piece of equipment, the real-time data stream that provides current condition readings, and the anomaly detection logic that identifies significant deviations from the baseline. For rotating equipment, vibration frequency analysis identifies the characteristic frequency patterns of bearing wear, imbalance, and misalignment. For thermal processes, temperature trend analysis identifies heat exchanger fouling, heater degradation, and insulation deterioration.
Shift and production performance reporting. Machine data processing provides the data foundation for the shift performance reports that production management uses — production counts achieved versus targets, OEE for the shift, downtime events and their causes, quality rejection rates. Automated shift reports generated from machine data replace the manual production log completion that operators currently perform and ensure that shift performance data is available as soon as the shift ends rather than after the manual data entry is completed.
Edge Processing and Cloud Architecture
Machine data volumes are large — a facility with hundreds of sensors generating data at one-second intervals produces millions of data points per day. Processing architecture needs to handle this volume efficiently while providing the low-latency access that real-time operational dashboards require.
Edge processing. For facilities where network bandwidth is limited, where processing latency requirements demand local computation, or where cloud connectivity cannot be assumed, edge processing deploys processing logic at the facility level — on industrial edge computers or on-premises servers. Edge processing filters, aggregates, and analyses machine data locally, transmitting only the processed results and significant events to central systems rather than the full raw data stream.
Edge-to-cloud architecture combines local processing for low-latency operational use cases — the dashboard that operators use on the shop floor — with cloud processing for analytics and historical analysis that does not require real-time response. The edge retains a rolling window of raw data for local troubleshooting while transmitting processed metrics and events to the cloud for long-term storage and analysis.
High-throughput ingestion. Machine data arrives at high frequency from many sources simultaneously. Ingestion infrastructure built in Rust handles the throughput — receiving data from OPC UA subscriptions, MQTT brokers, Modbus polls, and database integrations concurrently, applying the initial parsing and normalisation, and routing the normalised data to the processing pipeline. Rust's performance characteristics — no garbage collector pauses, deterministic memory management, minimal overhead — make it the right choice for the ingestion layer where consistent low-latency processing of high-volume data streams is the requirement.
Time-series storage. Machine data is fundamentally time-series data — measurements indexed by timestamp. Time-series storage is optimised for the write patterns that continuous data collection produces and the query patterns that operational analysis requires: the rolling time window query, the aggregation over a time period, the downsampling for historical trend display. We select and implement the storage architecture appropriate to the data volumes, query patterns, and retention requirements of each implementation.
Operational Dashboards
Machine data processing is only valuable if the processed data reaches the people who can act on it — in a form they can read and in the interfaces they use.
Shop floor operator displays. Operator-facing displays at machine level or work centre level — showing current OEE, current production count versus target, active downtime reason, and the alerts that require the operator's immediate attention. Designed for visibility at distance and for comprehension without detailed analysis — the traffic light status that tells the operator at a glance whether the machine is performing as expected.
Shift supervisor views. The shift-level performance view that the supervisor uses to manage production across their area of responsibility — the OEE and production performance for each work centre, the downtime events and their current status, the quality performance and any out-of-specification conditions, and the production target achievement for the shift in progress.
Engineering and maintenance dashboards. Detailed machine condition data for maintenance engineers — vibration trends, temperature profiles, cycle time histories, downtime event histories — with the analytical capability to investigate specific machines, specific time periods, and specific failure events. The data that root cause analysis, reliability improvement, and predictive maintenance programme management require.
Management reporting. Facility-level performance reporting for production management — OEE by line and by shift, production achievement versus plan, quality performance, and the trend data that manufacturing improvement programmes track over time.
Integration Points
ERP systems. Production order data from the ERP — the orders that provide the production context for machine data: which product is being produced, against which order, to what quantity. Production completion data from machine data processing fed back to the ERP — actual production counts, actual cycle times, actual scrap quantities — updating the ERP's production record with the data that machine systems capture directly.
Exact Online / AFAS / SAP. For manufacturers using Dutch ERP platforms alongside production systems, integration connects the production performance data that machine data processing produces with the financial and operational planning data that ERP systems manage.
MES systems. Manufacturing execution systems that manage the production workflow — work order dispatch, operator instructions, quality recording — integrated with machine data processing to provide the production context that enriches machine data and to receive the production performance data that machine systems generate.
Quality management systems. Quality inspection results — from CMM measurements, from in-line gauging, from end-of-line inspection — integrated with the process data that machine data processing captures, enabling the correlation analysis that quality improvement depends on.
CMMS platforms. Computerised maintenance management systems receive the maintenance trigger events that machine data processing generates — predictive maintenance alerts, threshold breach notifications, downtime event records — enabling maintenance work orders to be created from machine-generated signals rather than from manual inspection findings.
Technologies Used
- Rust — high-throughput machine data ingestion, OPC UA and MQTT client implementations, real-time processing engine, time-series computation
- C# / ASP.NET Core — SCADA and historian integration, MES and ERP connectivity, complex manufacturing logic, Windows-hosted edge processing services
- React / Next.js — shop floor dashboards, engineering analysis interfaces, management reporting views
- TypeScript — type-safe frontend and API code throughout
- SQL (PostgreSQL, MySQL, SQLite) — processed metrics storage, OEE history, downtime records, production reports
- Time-series databases — high-frequency sensor data storage optimised for time-series query patterns
- Redis — real-time metric state, alert processing, dashboard update coordination
- OPC UA — modern industrial machine data protocol connectivity
- MQTT — IIoT sensor and edge device data connectivity
- Modbus TCP/RTU — legacy industrial equipment connectivity
- OSIsoft PI / Wonderware / Ignition — process historian integration
- Exact Online / AFAS / SAP — ERP integration for production context and reporting
- REST / Webhooks — MES, QMS, and CMMS integration
From Data to Decisions
Manufacturing equipment generates the data that production improvement depends on — OEE analysis, root cause investigation, predictive maintenance, quality correlation. The difference between facilities that improve systematically and those that manage reactively is rarely the equipment or the engineering knowledge. It is the data infrastructure that makes the evidence visible and actionable. Machine data processing is that infrastructure.