We use cookies, similar technologies and tracking services

This website uses cookies, similar technologies and tracking services (hereinafter referred to as “Cookies”). We need your consent for Cookies, which not only serve to technically display our website, but also to enable the best possible use of our website and to improve it based on your user behavior, or to present content and marketing aligned with your interests. For these purposes, we cooperate with third-party providers (e.g. Salesforce, LinkedIn, Google, Microsoft, Piwik PRO). Through these partners you can also receive advertisements on other websites.
If you consent, you also accept certain subsequent processing of your personal data (e.g. storage of your IP address in profiles) and that our partners may transfer your data to the United States and, if applicable, to further countries. Such transfer involves the risk that authorities may access the data and that your rights may not be enforceable. Please select which Cookies we may use under ”Settings”. More information, particularly about your rights, e.g. to withdraw consent, is available in our Privacy Policy .

Settings

Only technically necessary Cookies

Accept everything

Below, you can activate/deactivate the individual technologies that are used on this website.

Accept All

Essential

These Cookies make a website usable by providing basic functions such as page navigation, language settings, Cookie preferences and access to protected areas of the website. Cookies in this category additionally ensure that the website complies with the applicable legal requirements and security standards. Owing to the essential nature of these Cookies, you cannot prevent their use on our website. Details about these Cookies are available under 'More information'.

Functionality and personalization

These Cookies collect information about your habits when using our web pages and help us to enhance your user experience by tailoring the functions and attractiveness of our web pages based on your previous visits, location and browser settings. They also enable access to integrated third-party tools on our website (e.g., Microsoft Azure for single sign-on authentication). This can involve transferring your data to the United States (for information on the risks involved read Clause 1.5 of our Privacy Policy). If you refuse these Cookies, you might not be able to access the full functionality of the website. Details about the tools we use are available under 'More information'.

Analysis

These Cookies are used to compile basic usage and user statistics based on how our web pages are used (e.g. via Google Tag Manager, Piwik PRO). If you accept these Cookies, you simultaneously consent to your data being processed and transmitted to the United States by services such as Salesforce Pardot (for information on the risks involved read Clause 1.5 of our Privacy Policy). Details about the tools we use are available under 'More information'.

Marketing and social media

These Cookies help third-party sources collect information about how you share content from our website on social media or provide analytical data about your user behavior when you move between social media platforms or between our social media campaigns and our web pages (e.g., LinkedIn Insights). Marketing Cookies from third-party sources also help us measure the effectiveness of our advertising on other websites (e.g. Google Ads, Microsoft Advertising). We use these Cookies to optimize how we deliver our content to you. The third-party sources and social media platforms we use can transfer your data to the United States (for information on the risks involved read Clause 1.5 of our Privacy Policy). If you accept these Cookies, you simultaneously consent to your data being transferred and processed as described above. Details about the tools we use and our social media presence are available under 'More information'.

More information

Save Settings

IIoT solution DXQequipment.analytics monitors paint booth online

Bietigheim-Bissingen, 30 January 2019 – Dürr DXQequipment.analytics brings transparency and intelligence to paint shops processes. The newly developed solution for the Industrial Internet of Things (IIoT) evaluates all robot and process data, traceable down to the millisecond, so that faults can be quickly identified and rectified. The technological highlight is a streaming analytics application for real-time data analysis.

What is happening in the painting cell and what is the condition of the technology? DXQequipment.analytics provides the answers. The software records and analyzes all relevant signals from the sensors and actuators integrated in Dürr painting robots. The application technology of the front robot arm, for example, permanently provides data on the pressure regulators, metering pumps and color valves. Main needles, turbine speed, shaping air and air heaters from the electrostatic rotary atomizers are also connected to the data recorder. The software also records the positions, torques, and temperatures of the painting and handling robots’ individual axes. Data from the conveyor technology is additionally collected for the positions of the bodies in the painting booth. This information is compared with data from the painting cells, such as start and end time of painting, or production data for individual bodies such as type and color code. All errors and warnings from the painting process are also collected.

Visual representation creates transparency

In addition to data recorder and database, the IIoT solution comprises a visualization for the graphical representation and analysis of the results. “With the help of the Visual Analytics module we can precisely trace the data from the previous weeks with millisecond accuracy,” explains Dr. Lars Friedrich, President and CEO of Dürr Systems AG. “The detailed information helps us to identify and eliminate faults quickly. The cross comparisons of different robots improve root cause analyses and help the plant operator to boost plant availability, increase the first-run rate, and improve the overall process sequence.” This creates transparency. For example, the signals collected from the metering pump together with flow rates, pressure regulators, and main needles of the atomizers can be displayed synchronously on the control computer and correlations can be defined. The software can display these process signals in a 3D view together with the trajectories traveled by the robots and the exact position of the body, and perform a comparison. By superimposing the signal curves, DXQequipment.analytics can also compare bodies by type and color.

Algorithms calculate the ideal plant condition and show anomalies

The additional module “Streaming Analytics” goes one step further: it analyzes the data in real time. Using algorithms, it calculates patterns and correlations that describe the ideal plant condition from the data collected. Even the smallest anomalies both within the plant and in the process are automatically identified. Employees can initiate corresponding service measures straight away and get to the bottom of the deviation before the body even leaves the paint booth. DXQequipment.analytics uses software from the IIoT platform ADAMOS for the streaming analytics function. Dürr operates ADAMOS together with Software AG and several mechanical engineering companies.

Model-based plant monitoring

“Streaming Analytics” provides the plant operator with easy-to-use graphical interfaces that do not require any programming expertise to create analysis models. For this, Dürr developed a Model Editor that provides the user with a library of analysis modules. These can also be individually combined into new models

(e.g. a model for monitoring the painting pressure during application). The data is displayed graphically on the screen. The operator can see that all values are within tolerance. With Streaming Analytics, the expert can create an analysis model that automatically determines anomalies such as air bubbles in the paint from the characteristics of the signal curve. These possible applications make the Streaming Analytics solution a powerful tool, since the software automates the plant operators’ process knowledge using online analyses.

Integration of machine learning in the future

“In parallel with Streaming Analytics, Dürr is developing a further module for Advanced Analytics that works on the basis of artificial neural networks. The software learns the optimal process condition fully automatically and registers every deviation. This means that the software will solve problems itself in the future through machine learning,” explains Dr. Friedrich. “If the self-learning modules detect trends, problems in the equipment can even be identified in advance by specifying a forecast of the event time.”

A number of car manufacturers have been using the DXQequipment.analytics software since 2018. The additional module Streaming Analytics based on real-time data has been used for the first time in a complete production line since the fourth quarter of 2018. The Advanced Analytics module, which uses artificial intelligence, is currently in the trial phase with a customer.

The DXQequipment.analytics software was developed in Dürr's Digital Factory. In this competence center, founded in early 2018, around 100 software experts are working on solutions for the digitization of production processes.

  • DXQequipment.analytics is the latest product from Dürr's Digital Factory. The software continuously collects and analyzes data from the paint shop, making painting processes transparent and efficient.

  • Paint robots equipped with sensors enable the acquisition of large amounts of data - an important driver for applications in the Industrial Internet of Things.

  • With the new Equipment Analytics software, the plant operator can monitor the condition in the painting cell.