Historical data analysis

With historical data analysis, you gain valuable insights into past data patterns and trends, which serve as the basis for well-founded decisions. In this way, you optimize processes, identify risks and increase efficiency in order to secure competitive advantages. Use the power of data to optimally prepare your production processes for the requirements of the future.
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Insights into data patterns and production workflows

Goals of a historical data analysis

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Identification of past data patterns & trends

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Investigating the interactions between data channels

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Easier data prioritisation for future analyses

Project procedure & price

Monthly schedule

Historical data analysis

from €15.000
Duration: Approx. 2 months
Analysis of operating and sensor data as well as synchronized time stamps
Door opener for further analyses & Proof of Concept
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*If step 1 or 2 has already been completed with sufficiently prepared historical data, the price can be reduced individually.
1

Data acquisition & preparation

Clarifying questions about machine understanding and collecting relevant historical data sources, e.g. operating, sensor, maintenance and error message data.
2

(Data) synchronisation and quality analysis

Data cleansing and synchronisation of various sources into a uniform data format and creation of a quality report across all data channels.
3

Data analysis & model training

Analysing the interactions between different data channels (e.g. operating conditions and sensor data) in order to identify correlations.
4

Interpretation of results & recommendation

Final results report with recommendations on the productive added value that the analyzed data can deliver in the future.

What are your possibilities after our historical data analysis?

The historical data analysis results report shows how your data can be used profitably in a live analysis with our software. Based on the analysis, you can either carry out a Proof of Concept (PoC) to test our solution within a defined framework or switch directly to the SaaS model. Of course, we support you with seamless integration into your IT environment. Together we can conduct a Proof of Concept (PoC), so you can test our solution within a defined framework or move directly to the SaaS model.
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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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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 the 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:
‍
Timestamp: Timestamp for the start and end of the fault or maintenance
Description of the cause: Description of the cause (e.g. mechanical defect, sensor problem)‍
Downtime details: Downtime information (planned vs. unplanned outages)
Measures: 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
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Any further questions?

Our expert Kim Barthel is happy to advise you!
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