Was muss aiomatic über meine Daten wissen?

Wir benötigen Angaben dazu, welche Datenfelder überwacht, ignoriert oder als externe Einflussgrößen betrachtet werden sollen. Ebenso ist es essenziell, den Zeitpunkt zu definieren, an dem der Prozess den gewünschten Zustand erreicht und die Daten dem Normalzustand entsprechen.

Welche Arten von Daten kann die Software von aiomatic verarbeiten?

Unsere Software kann Zahlen, Dezimalzahlen, Texte sowie Wahr/Falsch-Aussagen verarbeiten. Dabei berücksichtig sie sowohl Sensordaten als auch das Fachwissen des Ingenieurs, das über eine benutzerfreundliche Oberfläche eingegeben wird.

Was benötige ich, um mit der Datenerfassung zu beginnen?

Im ersten Schritt ist die Bereitstellung einer virtuellen Maschine oder alternativ die Installation der zusätzlichen Hardware erforderlich (siehe Möglichkeiten der Datenverbindung). Im zweiten Schritt erfolgt der Verbindungsaufbau zum OPC UA-Server der Maschine, wofür gegebenenfalls Netzwerkanpassungen und Portfreigaben erforderlich sind.

Welche technischen Voraussetzungen gibt es bei der Datenverbindung über eine virtuelle Maschine?

Für die Ausführung unseres Datenverbindungs-Clients auf einer virtuellen Maschine ist eine Linux-VM und eine VPN-Verbindung erforderlich.

Project Procedure for Predictive Maintenance Solutions

This is what distinguishes project work with aiomatic

Clear goals & structured approach

Timely milestones and provision of step-by-step instructions

Comprehensive onboarding & continuous support

With workshops and regular meetings, we steer your project specifically towards success

Highest safety standards

Your data is stored in the ISO/IEC 27001-certified Microsoft Azure cloud and managed in accordance with GDPR and EU AI regulation.

“We do not only offer a standardized, ready-to-use product for Predictive Maintenance, but also a holistic approach that helps companies optimize their production processes and grow sustainably.”

Lena Weirauch, CEO & Co-Founder of aiomatic
Photo: Henning von Holdt

The 4 phases to success

An overview of our project process

Preparation phase: Data collection

The establishment of data connection is done independently with standardized instructions from aiomatic. Our solution supports various digital interfaces - without additional hardware.

Preparation phase: Configuration

In this phase, the data channels are pre-sorted and the machine structure is set up. The aim is to define hierarchical levels of your machine (sub-areas, individual components) on which the health score is to be calculated.

Introduction of the application: Optimization cycles

The models are continuously trained with new data and optimizations are discussed with us in bi-weeklies. Events during plant operation, such as maintenance, faulty production, etc. are recorded and taken into account in training.

Introduction of the application: Scaling

A large part of the experience from the pilot plant can be transferred to structurally identical systems, which results in significant cost savings.

The biggest potential for you

Success factors for a quick & successful project start

For easy and fast implementation of our software and maximum added project value, we recommend these basics:

Machine data is already being digitally recorded periodically or on an event-based basis, at least every hour.

Machine data can be made available continuously via a digital interface.

IT staff from your team (possibly also shop floor IT or automation engineers) are available for implementation.

Support

Frequently asked

Here you will find answers to the most common questions about the process of a Predictive Maintenance project.

What are the different solutions offered by aiomatic?

In addition historical data analyses Do we offer one-day Workshops to plan your individual maintenance strategy If you are unsure whether your database is sufficient, we would be happy to provide a Proof of Concept with you so that you can test our software in your live environment. For long-term use of our software, our scalable SaaS model the right choice for you.

What steps are required for data collection?

1. Identify the machine or process that you want to monitor
2. Deploy the virtual machine and provide VPN access for our support
3. Release URLs to connect. Install the IoT Edge software environment.
4. Connect to the OPC UA server of the pilot plant and provide OPC UA information.
5. Set up the network connection between the virtual machine and the data source, including possible port sharing.
6. Use our detailed instructions, which also take into account special requirements such as a proxy.

What human resources are required on the company side?

For successful implementation, you need a plant operator, a person responsible for maintenance/repair, and an IT contact person for network approvals. In addition, a contact person from production may be required to provide OPC UA information.

Any further questions?

Our mechanical engineer Joel is happy to help you!

Get advice now!

Your path to success: Our project process