Detect errors in production lines and machines before they occur.

Predictive maintenance for more transparency and machine availability

With Predictive Maintenance, you can detect critical machine conditions in time before actual faults and production downtimes occur. Not only is it possible to predict when and where a fault might occur, but you are also supported in identifying the fault. With this procedure you will receive answers about the condition of your machine, when a failure is to be expected, which component will be affected, why the failure occurs and how you can avoid a failure.

Predictive Maintenance uses historic data of an asset to train a model for statistic analysis and machine learning. These models can be used to predict future behavior of the asset and help identify critical machine states to take countermeasures.

Predictive Maintenance von TIQ Solutions

Basic concept of Predictive Maintenance

Data collection at the production units

A production unit is equipped with a number of sensors and therefore monitored during operation. The resulting measurement data is stored in a database. They provide a complete historical description of the activity of the production unit and its environmental conditions. In addition, all occurring failure and maintenance events are recorded.

Data analysis

A mathematical model description of the data is generated by a computer-based analysis of the available data set. Special machine learning algorithms are used, which can automatically detect the correlations and patterns contained in the measurement data and map them with regard to the analysed faults.


By applying the learned model, a prediction for the future occurrence of errors can be made on the basis of the currently recorded measurement data. In the probability of critical operating states occurring, a warning can be issued in good time. This knowledge then flows into the planning and implementation of the next maintenance measure. In this way, components requiring maintenance can be replaced before they can fail, thus endangering the entire production process.

Example of use

Data sources

predictive maintenance data scource

Preparation and exploration of the
sensor and fault datan


Predictive Maintenance Vorgehen

1. Data exploration and visualization
2. creation of characteristic descriptions
and training examples
3. optimization of the model parameters


Predictive Maintenance Solution

Classification models enable the prediction
of faults and generation of alarm messages

Are you interested in our whitepaper?


    • Prevention of unplanned downtime with defect identification at component level
    • Improving machine availability and production quality
    • Longer service life of machines and production lines
    • Improvement of overall equipment effectiveness (OEE)
    • Reducing costs (resources)
    • continuous optimization of machines, processes and products
    • Improved planning of maintenance cycles to keep downtimes as short as possible
    • identification of advanced use case applications

    Implementation of Predictive Maintenance in your company

    We support you with a team of experienced Data Scientists starting with the planning, development and implementation up to the go-live and optimization of your Predictive Maintenance solution.

    Our three-step approach has proven its effectiveness in the implementation of Predictive Maintenance. The duration of the individual phases depends on your actual needs and can therefore be tailored to your individual requirements.

    Phase I: Initial workshop to determine the problem, the use case and the specification of requirements, processes and architectures as a basis for predictive maintenance.

    Phase II: Conception and modelling with prototypical implementation and following evaluation of the approach or method.

    Phase III: Integration of the solution into your IT landscape and implementation into your company, if necessary training of your employees and further project support during operation.

    How can we support you?

    Do not hesitate to contact us! We look forward to your message.