Wann sollte ich eine Machbarkeitsstudie (PoC) durchführen?

Wenn Sie sich noch unsicher sind, ob unsere Lösung optimal zu Ihren Anforderungen passt und Ihre Datengrundlage ausreichend ist, bieten wir Ihnen unseren Proof of Concept (PoC) an. So können Sie unsere Software in Ihrer spezifischen Umgebung testen und die Datenbasis evaluieren, bevor Sie sich für eine umfassende Live-Überwachung durch unsere SaaS-Lösung entscheiden.

Wie läuft eine Machbarkeitsstudie (PoC) ab?

In Monat 1 richten wir die Dateninfrastruktur ein und stellen alle Verbindungen her. Monat 2 widmet sich der Visualisierung und Datenerfassung, während wir in Monat 3 die KI-Modelle entwickeln und trainieren. Ab Monat 4 geht das System in den Live-Betrieb über, mit kontinuierlichen Optimierungen für beste Ergebnisse.

Welche Ergebnisse kann ich von einem PoC erwarten?

Im ersten PoC liegt der Fokus auf der Schaffung einer soliden Datengrundlage. Effizienzsteigerungen und Kostenreduktionen folgen in späteren Phasen, sobald die Modelle weiter optimiert werden. Wir empfehlen die Definition spezifischer, realistischer Ziele für den PoC.

SaaS model: real-time machine monitoring for optimum efficiency

The 4 phases to success

An overview of our SaaS model

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 Anomaly Index will 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.

Use cases from various industries

Large industrial machine in the energy sector

Gas storage & compressor

Monitoring of the entire system, in particular the fast-rotating, sensitive turbines.

Section of a machine for animal feed processing

Milling machines for animal feed

Monitoring of storage temperatures and drive performance for reliable & efficient production.

Illustration of a complex coating machine

Inline coating machine

Monitoring of the pumps required for the water cycle in the coating plant.

Download Use Case Portfolio

FAQ

Frequently asked questions

Here you will find answers to the most frequently asked questions about our historical data analysis.

What data is required for analysis?

For the analysis, we need:
‍
Operational data: Information on operating conditions, such as running times, system load, production rates and environmental effects.

‍Sensor data: Physical measurements such as temperature, oscillations, speed, or power consumption.‍

‍Maintenance and fault data: Historical records of failures, maintenance measures and their timestamps.

Technical specifications: Information on machine manufacturers, models and performance data.

In which format should the data be provided?

We prefer consistent file formats, in particular:
‍
CSV files: Time series data with clear column headings.

‍Alternatively: Excel or database exports (such as SQL, TimescaleDB). It is important that all data is provided with synchronized time stamps.‍

Which information is particularly important when it comes to fault and maintenance data?

The fault and maintenance data should include the following details:
‍
1. Timestamp for the start and end of the fault or maintenance

2. Description of the cause (e.g. mechanical defect, sensor problem)‍

3. Downtime information (planned vs. unplanned outages)

4. Remedial measures and their duration

What additional information helps with data analysis?

‍Machine specifications: Manufacturer, model, year of manufacture, tolerances and performance ranges

Sensor information: Position, type, manufacturer, and potential redundancies‍

Operating time pattern: Continuous or batch production, planned maintenance cycles. This information helps to correctly interpret the data and optimize analysis results

Any further questions?

Our mechanical engineer Joel is happy to help you!

Contact sales

SaaS model for maximum machine control

Objectives of long-term machine monitoring by aiomatic

Transparent machine insight for better control over production processes

Avoidance of machine breakdowns & production losses

Increased efficiency through identification of optimization potential