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Introducing the Control performance degradation detector

23/2/2021

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Modern plants and factories have hundreds or even thousands of PID and other control loops. Keeping track of the control performance  can be challenging but undetected control issues often have a very significant impact on product quality, production efficiency & volume, maintenance costs and can even impose safety & environmental risks.
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This is why we have developed a new module for Yanomaly: the Control Performance Degradation Detector (CPDD).
The CPDD detects degrading control performance of control loops and works with any (PID or other) controller.

Unlike CLPM (Control Loop Performance Monitoring) or CPA (Control Performance Analytics) tools, Yanomaly will not necessarily raise an alert or report an issue if a loop performs worse than some theoretical concept of ideal control. Instead it uses AI to learn for each control loop what the normal performance is, then only alerts if a control loop becomes worse. This avoids unnecessary alerts on control loops that always had a bit of oscillation or sluggishness because that was found acceptable or the best compromise.
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Sluggish control, Oscillations / bad tuning, Saturation, Stiction / static friction and many more common and less common control issues are detected automatically.
With its fast setup, auto-configuration for different loop dynamics and operating modes and its programming-free user interface, the CPDD delivers immediate value and retains Yanomaly’s flexible on-premise or in the cloud deployment and easy integration with any process data collection platform: OPC server, process data historian,…

It is sufficient that the CPDD has access to the trend data of the setpoint and process variable. If it also has access to the controller output and controller mode, it can detect even more control issues faster.

Yanomaly does not interfere with the control. It only reads and analyses the data and reports the results.

Results: Customers report that on average 5 important control or sensor or actuator issues are found per month, for on average one alert per day, per group of 300 control loops that are monitored by Yanomaly's CPDD. Investigating an alert only takes a few minutes because the CPDD is specific about the type of issue that it has found, and with one click the user sees the relevant trend plots in the user-interface for further investigation.
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Want to know more? Contact Us!
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Hybrid Machine Learning for Smart Maintenance webinar

23/2/2021

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Join us at the free Webinar "Hybrid Machine Learning for Smart Maintenance" on 25th March 2021 from 9 till 11 am for ​insights in leading edge data science technologies in the domain of industrial maintenance.

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VLAIO GRANTS SUPPORT FOR YANOMALY R&D FOR THE NEXT TWO YEARS

6/1/2021

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Thanks to three project grants, Yazzoom will be able to accelerate the development of its AI-powered industrial data analytics solution Yanomaly:
  • CONSCIOUS - Contextual Anomaly Detection for Complex Industrial Assets focusses on the detection of abnormal behaviour in industrial machines and processes through the analysis of sensor data.
  • TRACY  - Trace Analytics aims to use AI and machine learning to leverage data contained in log files generated by industrial equipment. 
  • CoSMoS - Concrete Strength Monitoring Sensors has for goal to develop sensors that will be used in combination with AI on a  cloud IoT platform to determine concrete curing in the first few days after pooring the concrete, and concrete aging for long term monitoring.
All projects will use real-world industrial use cases, including compressors, assembly lines, and industrial printing systems.
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EXCELLENT RESULTS FOR YAZZOOM AND PARTNERS IN THE TWO PREDICTIVE MAINTENANCE HACKATONS OF THE ASSET PERFORMANCE 4.0 CONFERENCE

14/10/2020

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​We are very proud to announce we are the winner of the Fluvius challenge to predict failures in electrical substations.
We ended up second (out of 13 contenders) in the Aquafin challenge to predict failures in pumping stations.
In both cases we collaborated with domain experts to combine the best of Artificial Intelligence and Human Expertise: in the Fluvius case with Engie-Laborelec and in the Aquafin case with i-Care.

If you haven't been able to follow the presentations during the conference, you can find the videos used in those presentations  here.
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Watch a recording of the ASSET Performance Presentation on Improving the OEE of machines, assembly lines and packaging lines using advanced analytics on the binary signals of the control software

14/10/2020

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More application domains

21/9/2020

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Learn more about other domains of application for machine and asset data analytics here.
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How can Machine Data Analytics and Anomaly detection help your business?

21/9/2020

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Follow the link to read about use cases for:
  • Equipment manufacturers
  • Process industry and continuous production
  • Batch production and discrete manufacturing
  • Utilities and power generation
  • Iot, monitoring and data collection platform vendors
  • Third party equipment monitoring and service providers
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EXCELLENT RESULTS FOR YAZZOOM AND PARTNERS IN THE TWO PREDICTIVE MAINTENANCE HACKATONS OF THE ASSET PERFORMANCE 4.0 CONFERENCE

21/9/2020

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We are very proud to announce we are the winner of the Fluvius challenge to predict failures in electrical substations. We ended up second (out of 13 contenders) in the Aquafin challenge to predict failures in pumping stations.
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In both cases we collaborated with domain experts to combine the best of Artificial Intelligence and Human Expertise: in the Fluvius case with Engie-Laborelec and in the Aquafin case with i-Care.

If you haven't been able to follow the presentations during the conference, you can already find our video demonstrations of Yanomaly as featured during the competition by following this link.
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Download the latest YANOMALY FLYER

21/9/2020

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TIETOEVRY AND YAZZOOM PARTNERSHIP

2/9/2020

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​TietoEVRY and Yazzoom partnership promotes the use of Artificial Intelligence in the pulp, paper and fibre industry.
Data analysis in mills has a long history, but new technologies are making the next level possible through AI and machine learning. Yazzoom provides a rich set of tools and algorithms, from simple single sensor anomaly detection all the way to real-time predictive modelling and optimisation. TietoEVRY will embed these capabilities in its TIPS industry solutions and services. ​Click for more.
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Customer stories BROCHURE: AI in the industry

14/12/2018

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Includes 3 customer stories using Yanomaly.
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Jan explains yanomaly at IoT Tech Expo Amsterdam 2018

24/9/2018

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White paper: WHAT DOES ARTIFICIAL INTELLIGENCE HAVE TO DO WITH ASSET MAINTENANCE?

5/9/2018

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​Asset monitoring systems based on artificial intelligence allow industries to reduce unplanned downtime, reduce mean time-to-repair, and increase the return on investment of maintenance operations in both the manufacturing and process industries.
Download.

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IDev40 Research Project "Integrated Development 4.0​"

5/9/2018

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In this project, 38 partners from six countries will be involved in research on the smart networking of development and production processes for electronic components and systems.
Development and production teams will be networked with each other, independent of their locations, and communicate in real time along the value chain. Processes can be virtually mapped by digital factory and product twins and can thus be simulated comprehensively.
Artificial intelligence and machine learning play a central role in all of this.

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WATER-LINK DEMONSTRATES THE POWER OF PREDICTIVE SOFTWARE FOR ITS WATER NETWORK WITH THE HELP OF YAZZOOM.

5/9/2018

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An article from our customer Water-Link, on the Proof of Concept project on the detection of anomalies in the water network of Antwerp.

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ITEA3 Reflexion Project "React to Effects Fast by Learning, Evaluation, and eXtracted InformatiON"

5/9/2018

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Reflexion will optimise the full end to end product development lifecycle and maintenance process, bringing in analytics to automate and complement expert knowledge, and enabling predictive maintenance on a broader industrial scale and shortening product evolution development iterations.

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Yazzoom's Anomaly Detection for the ESI Demonstrator

5/9/2018

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The demonstrator is based on a virtual industrial system (i.e., a production line with multiple machines), which has the common functionalities of high-tech systems, such as lithography machines and professional printers, etc. We have developed a simulator of the system and generate the operational data. As an industrial showcase, ESI’s demonstrator presents “Smart high-tech system-diagnostics with operational data”. 
​One of the fundamental issues of root-cause analysis is how to detect anomalies from data. Yazzoom’s advanced unsupervised anomaly detection techniques have been applied by ESI’s demonstrator in the following two aspects.

​To diagnose a specific machine issue and narrow down the space of causes, we use the operational data generated by the machine components. Basically, anomalies are identified based on the operational data. This is achieved using unsupervised learning techniques, which are able to detect anomalies without efforts from domain experts. 

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Download the Poster: Machine Data Analytics & Product Life Cycle

4/9/2018

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​Machines, systems and devices generate more and more data, and collecting this data centrally (by using IoT or any other data platform), or analysing it on premises are also both easier than ever before.
Under the form of sensor readings and log files containing information about internal processes steps, performance or user interaction for example, but also data external to the equipment, such as the properties of raw materials or ambient temperature and characteristics of the electrical network powering the system, this massive amount of information contains valuable, actionable insights.
Advances in machine learning and artificial intelligence make it possible to use this data to enhance most aspects and phases of a product’s life cycle.
​Download.
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