Each system holds one piece of the story. No one sees the whole machine on the same clock.
SCADA and PLC
Current, temperature, pressure and alarms.
2:03 PMOct 13 · 14:03
temperature rises
Standalone sensors
Vibration and oil, each with its own software.
10/13 14:03:22Oct 13 · 14:03
vibration rises
Work orders
What broke, what was replaced, how long it was down.
WO opened Oct 13Oct 13 · 14:03
bearing replaced
Spreadsheets and shift log
What the operator saw and wrote down.
shift BOct 13 · 14:03
bearing noise
On the same timeline, the story emerges.
From signal to decision
Four steps, in your environment.
We start with what you already have. A new sensor only if needed.
01
Connect
We read the PLC, SCADA, sensors, spreadsheets and work orders. If a measurement is missing, we first look for a solution using what is already there. If there isn’t one, a connected (IoT) sensor is custom-made, and we can install it.
02
Organize
Each data point gets a timestamp, a unit and a source. A gap stays flagged, never filled in silently.
03
Analyze
We learn what normal looks like for your machine from its own history. A deviation becomes an alert, with its reason.
04
Decide and record
You see the data, the assumptions and the margin. The decision and its result are added to the history.
↑ the result feeds back into the analysis
the result feeds back into the analysis
Secure management
Safe for the machine. Safe for the data.
The machine is your asset. So is the data about it.
The machine
Read-only first: data collection starts without touching the machine’s controls.
Your team decides: the system recommends; operations approves.
Stated limits: each alert says which limit was crossed and where that limit comes from.
The data
In your environment: the data leaves your infrastructure only if you choose.
The platform is yours: you know what runs and can audit it.
Record: each decision is stored with the data that supported it.
Services
From diagnosis to your own platform.
Serra Pereira does not sell software. We sell knowledge of control and analysis. First we define the work. The timeline comes after, with the scope in hand.
Machines and data
For electromechanical machines that can’t stop without warning: motors, pumps, compressors, generators and wind turbines.
Data assessment
What the machine already measures, where that data is and what is missing. Gaps, stuck sensors and out-of-range readings show up before the decision.
Integration and sensors (IoT)
Different sources in one database. A new, custom-made sensor only if your operation needs it.
Predictive analytics and alerts
The normal pattern of each machine, the deviation and the margin.
Cost calculation
The methodology to find where the operation loses money today. Investment analysis is one way to analyze cost.
Shutdown planning
The intervention, coordinated with production, in the window you choose.
Your platform, with AI
How to instruct AI so your team can build its own platform. It runs in your environment.
Curtailment
For solar and wind plants whose output is curtailed by the system operator. Curtailed energy is treated as idle raw material.
Forecast: curtailment over the next 48 hours.
Metering: an estimate of what CCEE will settle.
Operations: when to stop each inverter or wind turbine.
Investment: a stated rule, the price each route must sell at, and its payback period.
The demos use public data from ONS and CCEE and a simulated plant. They are not formal engineering reports.
How we work
The problem first. Then the analysis.
The person you talk to is the one who does the math.
01
Understand
We talk with operators and maintenance staff before running any numbers.
02
Design
From data to decision and record, in your environment.
03
Check
Every calculation is redone outside the system, in a spreadsheet.
screen▮▮.▮=spreadsheet▮▮.▮
04
Disclose
Assumptions and uncertainty are written next to the number.
Who does the work
Engineering by someone who likes machines.
Fabio Serra Pereira holds a degree in Mechatronics Engineering from Poli-USP and a master’s in Mechanical Engineering from TU Darmstadt, in Germany. He takes part in every stage, from planning to hands-on work.
FieldDeveloped industrial data acquisition systems and deployed asset management platforms, adapted to each operation.
GermanyEntrepreneurial experience with Industry 4.0 virtualization.
RenewablesPredictive analytics applied to electromechanical equipment, with a recent focus on solar and wind energy, in the United States and Brazil.
