SaaS model for maximum machine control

Our SaaS model goes beyond a one-off analysis of historical data: the software is integrated into your existing IT landscape on a long-term basis and analyses your machine data around the clock for deviations after a short learning phase. As a result, you benefit from long-term relief for your maintenance staff, early problem detection, and maximum efficiency.
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Maintenance personnel analyzes historical data

Objectives of long-term machine monitoring by aiomatic

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Transparent machine insight for better control over production processes

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Avoidance of machine breakdowns & production losses

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Increased efficiency through identification of optimization potential

The 4 phases to success

An overview of our SaaS model

1

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.
2

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 will be calculated.
3

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.
4

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.

Overview of the various SaaS packages

Silver
Starting at 5€
per data channel & month
+ €1,999 set-up
Check-in meetings with aiomatic experts: monthly
Number of model trainings:
1 model training/month
Sampling period: > 60 seconds
Personal assistance with
Onboarding: 1 hour
Gold
Starting at 15€
per data channel & month
+ €2,499 set-up
Check-in meetings with aiomatic experts: twice a month
Number of model trainings:
3 model trainings/month
Sampling period: > 10 seconds
Personal assistance with
Onboarding: 3 hours
Elite
Starting at 25€
per data channel & month
+ €4,999 set-up
Check-in meetings with aiomatic experts: weekly
Number of model trainings:
5 model trainings/month
Sampling period: > high frequency
Personal assistance with
Onboarding: 5 hours
Enterprise
Individual price request
Your solution to monitor multiple machines & get more insights
Check-in meetings with experts
Number of model trainings: individual
Sampling period: > high frequency
Individual, personal advice
& Support
Individual request

Successful reference projects

From identifying the use case to implementation and continuous optimization of the maintenance plan
- we accompany you through every step of your Predictive Maintenance project.
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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 system
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
Employee of aiomatic
Any further questions?
Our expert Kim Barthel is happy to advise you!
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