A lot of complex things are very simple if you think them through, or so they say. The history of science is full of discoveries, techniques and methodologies helping to research problems caused by malfunctioning devices and systems. It includes examining a lot of components, subsystems and assemblies to find out the nature of failures, their causes and effects. 

What is necessary for efficient operation

Running mission-critical applications and working in a highly-constrained environment leaves no room for failure and requires a solution that: 

1. pinpoints a problem with a high level of accuracy, yet tells the exact cause in real time; 
2. equips with robust mechanisms allowing prediction and prevention of potential problems.

You know it – sensors sample data, send it to a server in the cloud to store, and then make available for analysis that produces some actions. However, applying it to IIoT mission-critical applications and running it in an asset-intensive industrial environment is totally impractical. The outcome is rather chilly = 1000 samples/sec x 1Kb data x 10,000 sensors = 10GB data/sec = a Category 5 hurricane in the making – and you’d better be ready.

VixteraIIoTEdge appliance offers a patented methodology to govern with ease the enormous amount of data generated by a multitude of devices, trending and tiering it by priorities, rules and policies whereas analyzing and identifying in real time the root cause of the problem. It uses causes for automatic generation of training data sets while eliminating human involvement from complex AI processes and enabling development of new applications and services.

The benefits of IIoT data curation

What is your experience so far dealing with an overflow of IIoT ill-tempered data? Well, we’re all learning together that one can’t blindly grind through an enormous pile of data without developing a way to sort them out separating “good ones” from the rest that are practically useless.

Besides improving operational efficiency by bringing data governance closer to action, reducing backend cost and solving critical latency-depending issues, Data Curation provides you with means to organize, describe and clean the data while leveraging knowledge of your asset and enhancing benefits of your applications and services.

Vixtera introduces a new critical edition to its ViEdge software framework enabling multifaceted data curation using an innovative correlation algorithm to prob, analyze and clean collected data giving users a holistic view across sensors, devices and content. As a result, it: 

• eliminates duplication of metadata whereas reducing the frequency of transmission and size of collected data;
• contextualizes and aggregates relevant events yielding meaningful information to the outcome-based applications while tuning and optimizing performance of the end2end ecosystem.

To scale, agents can be integrated with communications platforms complementing it with full IIoT capabilities. 

How to extract useful data

The industrial digitalization presents us with a profuse dilemma in defining values and boundaries of your data. It’s said that, while collecting data from industrial devices, the large amount of it is incoherent, irrelevant and is practically useless. Besides the overwhelming network with flawed information, it does create enormous challenges for real-time and predictive analysis.

Curating data at the edge resolves significant issues exposed by bandwidth and latency. However, it often involves algorithmic complexity that requires high-performance computing systems to process. 

VixteraIIoTEdge uses a lightweight method of multifaceted data curation employing tiny, agile and configurable agents. The agent runs on a small memory footprint applying innovative trend analysis and curbing data streams while filtering and grouping (e.g., context) relevant events. Thus, it eliminates duplication of metadata whereas reducing the frequency of transmission and size of collected data. 

From the get-go, it produces meaningful information for real-time analysis at the edge while supplying reliable well-structured data for AI/ML modeling and predictive analysis anywhere in the network. 

How to yield reliable data to drive trustworthy AI 

The industrial IoT systems become more pervasive and require a multitude of analysis to drive the outcome for processing mission-critical applications. When it comes to all things predictive, it involves training of AI system which is fed on the enormous amount of unstructured data and… manual labor. So, it is IMPRACTICAL to:

1. train data sets for AI banking on a barrage of repetitive, incoherent and irrelevant events in the logical enclosure of connected devices. Moreover, over 50% of events come from misconfigured devices and “noisy” sensors that must be “tuned”
2. generate AI training set using manual error-prone labeling  
3. depend on AI models for real-time analysis

VixteraIIoTEdge uses patented algorithms performing it closer to devices, providing for real-time failure analysis and utilizing cause of failure for auto-generation of AI training data sets. It accurately pinpoints a problem and allows altering it in real time while providing reliable labeling source for AI learning. Thus, it drives consistently-good quality data for a variety of predictive applications and services.

So what?

Using these tips, you’ll be able to get sorted and relevant data. Saving and analyzing only properly filtered information will reduce the memory that is needed for its processing. It will accelerate the work of the whole system. Another advantage is that there will be no redundant data for analysis. It will help to increase its quality and show the real state of things. For more information visit our website.