Intelligent data processing
How to extract the signals that matter for maintenance from growing streams of sensor data — automatically, in real time, instead of reviewing tables by hand.
What intelligent processing is
Intelligent processing refers to operations performed on data — usually on large volumes arriving in a short time. The key is that the analysis happens on the fly, not after hours of manual compilation. This quickly turns raw readings into information: this machine is starting to overheat, vibration is rising faster than usual, this deviation is out of range.
Why it is needed in maintenance
A single machine with sensors can generate thousands of readings per minute. Watching such data by hand is impossible, and what matters most is often not the values themselves but the trends and deviations from the norm. That is why data is processed automatically, using among others:
- Stream analysis — data is assessed on the fly, as it arrives.
- Thresholds and rules — crossing a limit triggers an alert or work order.
- Anomaly detection — a model learns the norm and flags deviations.
- Forecasting (ML) — estimating when a parameter will reach a critical level.
Intelligent processing in SimplyMobile
In SimplyMobile, intelligent processing most often concerns data from IoT sensors and machine telemetry. The results do not end at a chart — they connect to the workflow: they create work orders, feed KPIs and the Simply ML prediction module, and the SaMi assistant lets you ask about the data in natural language. It all runs in the Azure cloud or on-premise.
Frequently asked questions — data processing
- How does real-time processing differ from reports?
A report is produced after the fact, from historical data. Real-time processing assesses data as it arrives, so the response (alert, work order) happens immediately — before the problem develops.
- Is intelligent processing the same as AI?
Not exactly. These are methods of working with data — from simple thresholds and rules, through stream analysis, to machine-learning models. ML/AI is one of the tools used in this processing, especially for anomaly detection and forecasting.
- What do the results give me in practice?
Concrete outcomes: automatic work orders once thresholds are exceeded, earlier warnings about deteriorating machine condition, and data for KPIs and failure prediction — meaning fewer unplanned downtimes.
See SimplyMobile in action
Book a free, no-obligation demo — we'll show the system on your own processes.