Why process engineers use aivis
Industrial data-driven challenges are demanding: Thousands of signals, terabytes of operations data, and highly dynamic processes meet the highest requirements in quality, reliability, and safety. Time to get you the right tool!
As a process engineer, you are responsible for ensuring a smooth and efficient production process. A decisive factor in this is operational data, which is full of information helping you identify errors, prevent disruptions, counteract
negative trends and further optimize processes.
But getting this information out of the data is challenging, especially if your access to data science is limited. So, how about an AI tool that extracts the information for you?
How about a powerful AutoML technology that does most of the work for you and requires no science expertise? An AI tool that improves your process understanding and enables you to focus on the right questions instead of how to answer them?
How about aivis?
aivis does all of that and more: Its powerful AutoML technology core copes extraordinarily well with terabytes of operations data, including thousands of signals, while focussing on simplicity and automation.
Just import your data and define your goal, everything else the AI does for you: Finding critical relations and hidden patterns, investigating response times, creating models, and much more.
aivis provides you with either a report or a model accompanied by a report, making all results highly explainable and actionable. In detail, they set out which influencing factors and relationships are of crucial importance. This way, aivis not only supports you at monitoring critical components, preventing disruptions, and predicting process parameters; it helps you to unveil unknown relations and increases your process understanding.
Those are the main reasons, why using aivis is a great idea for process engineers.
Just import your raw data using the aivis CSV format. aivis requires no data cleaning, time-series synchronization, or prior exclusion of supposedly irrelevant data portions.
All results include a report that shows which signals or parameters contribute to which extent, making them easily understandable and interpretable to the domain expert.
aivis autonomously unearths hidden patterns and previously unknown relationships, deepening the process understanding and enabling immediate process improvements.
Simple to use
Working with aivis requires no data science expertise, and No pre-knowledge has to be inserted. All you need is a general understanding of your processes and your data and a goal.
Questions and answers
I believe that I could get more out of my operational data than I do today.
Great! The amount of operational data collected and stored has grown considerably in recent years. Still, the benefits of this data are so far only exploited to a fraction, which means losing money! AI helps to increase this benefit significantly.
OK, so I need AI.
Yes. More precisely, you need machine learning, which is a part of AI. Machine learning is a process that uses an algorithm to analyze data, learn from it, and make a statement or prediction. And even more precisely, you need automated machine learning or AutoML, which automates the process of applying machine learning to real-world problems.
Alright, so I need an AutoML solution.
Yes, but not any will do. Operational data is complex. It can include terabytes of data, thousands of unsynchronized signals and may have varying data quality. So you require AutoML that can cope with all that and handle the complexity of real-world industrial problems, like aivis. You need AutoML that produces excellent results despite those challenging conditions within few minutes, not hours or even days.
But I am an engineer, not a data scientist.
Don’t worry, an excellent AutoML technology like aivis does most of the work for you. It does not require any data science expertise from you, only some basic understanding of the process and the data coming from it.
OK, but what about data preparation?
With aivis, it is kept to a minimum. All you have to do is bring your raw data into the correct CSV data format – or not even that if you are using one of the aivis IIoT platform partners. In any case, you don’t have to filter your data according to your problem, and you don’t have to clean or synchronize your data. Just put everything in you have. This way, aivis has the best chance to answer your questions and even find hidden patterns.
But I don’t have a lot of data.
No problem, as long as you have even a little bit of data, you can start using aivis. And if the result is that what you were asking for cannot be answered from your data, this is also a valuable result.
But what about my process knowledge?
aivis doesn’t require any pre-knowledge for the analysis of the data. Instead, it follows a strict goal-oriented approach, where all you have to do is define your goal and start aivis. Everything else is done completely automated. In fact, at the beginning you have to use your knowledge to verify the results aivis presents to you. This will build up your trust when aivis presents insights that you haven’t known yet.
So I can understand the results?
Absolutely! aivis results are always fully explainable, whether it is a model or an insight report. This is because explainability and transparency are part of the basic functioning of aivis. In fact, aivis works oppositely to most conventional approaches to ensure just that.
Wait a minute! Does aivis know my processes better than I do?
No. aivis doesn’t know about the chemistry, physics, or statics of your processes. Instead, it gains process insights by looking at how different signals and sensors react and respond to each other, including time lags. This way, it can find and reveal fundamental and complex relations and dependencies, helping you to improve your knowledge. As the process and domain expert, you are still responsible for interpreting the results, deriving actions, and defining countermeasures.
What is the outcome of aivis?
aivis has two primary outcomes, models and reports. The outcome is a report if you want aivis to analyze your data and investigate a specific signal, quantity, or disruptive event. On the other hand, if you wish to monitor critical components or processes or create virtual sensors, the outcome is a model accompanied by a report explaining the model. The model is a lightweight, independent software module deployable on-premise, on-cloud, or on-edge to consume live operations data.
How is this related?
Find influencing factors, signal dependencies, and root causes for events.
Detect bad behavior
Watch critical components and systems and detect abnormal behavior in time.
What will happen?
Create soft sensors to predict lab measurements and other quantities.
React to emerging errors in time to apply appropriate countermeasures.