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5 min read
Effective anomaly detection using artificial intelligence
Automation significantly increases productivity, but unprecedented failures in - often complex - systems that provide automation result in significant financial losses. - Your choice?

Do not accept this trade-off "automatically"! Can we help you?
In almost every aspect of our lives, we expect to be served by automated solutions. We like, we aspire to have boring, monotonous, repetitive tasks done for us by machines, software, and robots. Doesn't that make us feel good? We expect to be able to use the energy so freed up to perform creative, far more exciting tasks adding higher value. However, as soon as we create these automatisms and machines that are supposed to make our lives easier, the possibility of uncontrolled anomalies hidden from us emerges.
What if one of the components of the software or machine parts we create breaks down and we do not get the results we expect, or we do nöt get them the way we expect?
Obviously, we want to be prepared for this in advance, so we incorporate various rules and constraints into their operation to indicate whenever there is a failure or a data point outside the predefined limits, and we receive alerts, information, and notifications. Based on the information and knowledge we get, we can remedy the situation or call in help to correct the malfunction and we are happy again.
But can we reasonably hope to write down all anomalies, all unexpected events, prior to their occurrence with rules? What happens if our automated system produces so far unprecedented results or we run into heretofore unknown anomaly events?
We start guessing, investigating, thinking, trying, and scratching our heads. This recognition of unknown, abnormal functioning is called anomaly detection, and the identification of the underlying problem is called root cause analysis and exposure. Since no useful information is available, this process, based solely on experience and previously accumulated professional knowledge, can often turn out to be long and frustrating, usually involving serious financial loss.
How can we remedy the situation? Our answer is data-based anomaly detection and root cause analysis. Let's employ a piece of software to help us out.
Measuring the operating parameters of our machines, the characteristics of the environmental conditions, and collecting this data is no longer complicated. All technologies and methodologies are available in a tested and proven way. Once the measurement and data collection environment is established, we can immediately have access to machine-assisted, artificial intelligence-based anomaly detection and root cause analysis.
This is the solution we offer!
Anomaly detection is an AI system based on unsupervised learning that can detect different patterns compared to the data set describing normal operation. In essence, this allows us to identify any fault in the operation that can be extracted from the available data set. Anomaly phenomena already experienced before (known, typical faults), as well as anomalies that had never occurred before, can be detected, too. Expertise is nevertheless still needed. Knowing normal usage routines and standard operating practices, and based on professional background knowledge, it is possible to interpret data sets that reflect anomalies and assign anomaly types to them, or simply classify them as "interesting data patterns" (noise). And this brings us to the notion of an automatic anomaly detection system.
Anomaly detection and exposure based on this principle can be, and is, applied to many areas of life. Think of how banks can screen out when someone tries to make a purchase with a stolen credit card, or how a manufacturing company can check the quality of a manufactured product when it is described by 20-50 parameters at the same time, or how a solar farm can discover why the amount of energy produced is less than the theoretical maximum. The list of examples goes on.
Most importantly, the monitoring and anomaly detection systems built using artificial intelligence solutions can detect yet unknown failures that cannot be described by rules specified beforehand, which in turn speeds up the recovery process and thus reduces costs.
What is more, these previously unrecorded deviations can be used to further teach and educate the system, eliminating the need to write new rules every time. In addition, the technology can not only detect anomalies that are already certain to occur, but can also predict the improper, abnormal functioning of a component in a process and alert the operator before the failure occurs, thus creating the opportunity to prevent the problem.
Do not leave loopholes open!