True Predictive Maintenance Thanks to Innovative Technology

Interview published in atp magazine 08/2018

Since the 2018 ACHEMA trade show, SAMSON has been able to offer a predictive monitoring and diagnostic system – Precognize – thanks to the acquisition of the Israeli start-up company Visual Process Ltd. In an interview with ATP magazine, Mr. Chen Linchevski, CEO of Visual Process Ltd., Dr. Andreas Widl, CEO of SAMSON AG, and Dr. Thomas Steckenreiter, executive board member for research and development of SAMSON AG, talk about the benefits of this new technology and why it takes predictive maintenance to a whole new level.

Mr. Linchevski, Dr. Widl and Dr. Steckenreiter, with Precognize you have added a software tool for predictive maintenance to the range of digital solutions that SAMSON offers. What makes Precognize so special?

Chen Linchevski: Precognize makes it possible for us to comprehensively analyze the large amounts of data that are generated in an industrial plant. We are now able to detect and remedy faults and anomalies that are deeply rooted in the plant structure.

Dr. Andreas Widl: In addition, the technology allows us to compile the enormous know-how that the plant owners and operators possess on the topology and processes in their plants and reproduce it in a clearly structured digital model.

Dr. Thomas Steckenreiter: We offer the first solution that brings together this complex topology of different field units with the alarms and error messages documented in their data.

Solutions for predictive maintenance are nothing new in the era of the IIoT and smart plants. How is Precognize different from the other software tools available on the market?

Linchevski: Our solution offers true predictive maintenance. We analyze the data, detect deviations and are capable of relating them to certain field units.

How does this work exactly?

Linchevski: There is no way of reliably predicting malfunctions or failures of a valve, for example. What we can see though is that there are changes in the measured values and data recorded by the sensors of this valve. The deviations – we call them anomalies – accumulate.

Many other software tools can also detect deviations.

Widl: Yes, that's true. But the problem with these tools is that they show the anomalies to the plant operators without any filtering. Plant operators realize that something isn't working the way it should do but they still cannot say what really causes the problems. With our solution, we can pinpoint the exact cause of the anomalies – right down to sensor level.

How is that achieved?

Linchevski: With SAM GUARD, a software tool based on Precognize, we can cover the topology of a single machine, a plant or an entire works site. We scan the plant for about two weeks and learn how the plant operator runs which processes. After that, we have an exact idea of which valve and which sensor is involved in which process.

So you create something like a digital twin of the entire plant?

Linchevski: I would rather call it a 'digital map'. We relate this digital topology to our algorithm, which permanently analyzes the constant stream of data. In reality, this enables us to single out anomalies and, based on the digital model, we can indicate whether they point to a specific problem.

Is this the core of Precognize?

Linchevski: Exactly. The core strength of our technology is to predict which of the sometimes thousands of field units in a plant will have to be inspected, replaced or repaired in the future.

How much artificial intelligence (AI) is contained in Precognize?

Linchevski: The technology is based on an algorithm, which assigns the anomalies and relates or links them to certain field units. To give the algorithm the capabilities it needs, we need to feed it with historical data. Based on these data, the algorithm generates normality clusters. This means it collects more and more details to detect the limits of normal operation, which allows for a quick and reliable identification of any new malfunctions or failures that occur. In a second step, we can assign the anomalies based on the digital model of the plant and visualize them using a human-machine interface (HMI).

So you transform anomalies into visible problems.

Steckenreiter: You could say that. By solving these problems, our technology can profoundly optimize plant operation: the topology linking reveals previously unknown dependencies between field units and machinery to the plant operators, who can prevent malfunctions in plant operation in the future.

Linchevski: In addition, Precognize learns with every new anomaly and demands operator feedback to develop further. The system learns something new from every failure, regardless of the type of field unit that is being used.

The system is not tailored to certain devices?

Steckenreiter: No, the system can be used regardless of the device types to be monitored: You can use it to monitor sensors, valves, pumps or entire plants with several thousand field units of different quality levels. The employed methods are always the same.

Widl: Another great advantage is that different user groups can work with different areas of the tool. For example, it is possible that the sensor service team only has access to the sensor monitor in the HMI while the valve team only sees the valve monitor.

If the tool is independent of the field unit type: is it also independent of certain semantics?

Linchevski: Absolutely. We need to speak the language of our customers. Otherwise, our tool just wouldn't be accepted in the market. We are not limited to certain semantics, neither when creating the digital model nor for the analyses. Our software supports all common machine languages.

Can Precognize also serve as the connecting element between different island systems within a plant?

Linchevski: Yes, this is definitely an option. It is really not a question of technology but rather of the business model. Precognize lets us interrelate so many data and creates so many opportunities that it will take us another few years to fully exploit them. We are just at the beginning.

What will Precognize look like in a few years? Which road map are you pursuing?

Linchevski: Our hope for the future is that, in addition to predictive maintenance, we will also be capable of providing prescriptive maintenance. This would be the next step in our evolution. The tool would not only detect an anomaly and assign it to a specific field unit, it would already start the necessary processes as well./p>

Chen Linchevski

is co-founder and CEO of Visual Process. Before, Mr. Linchevski was CEO of Opcat and held executive positions at Ayeca, which specialize in the analysis and design of complex business analyses. He studied law at the Hebrew University of Jerusalem.

Dr. Andreas Widl

was appointed to the executive board of SAMSON AKTIENGESELLSCHAFT in 2013. On 1 April 2015, he took over as CEO and chairman of the board. Before joining SAMSON, Dr. Widl had held executive positions at Mannesmann, GE Capital and the Swiss Oerlikon group.

Dr. Thomas Steckenreiter

was appointed to the SAMSON executive board on 1 May 2017. Before, he had held executive positions at Endress+Hauser Conducta and Bayer Technology Services (BTS). Dr. Steckenreiter had also served on the NAMUR board of directors before he joined SAMSON.

This article was published in atp magazine.

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