Artificial Intelligence and IIOT: The Perfect Combination
Is artificial intelligence (AI) the best option for industrial applications such as the Industrial Internet of Things (IIoT), or is there a better option?
There are numerous areas where artificial intelligence can be extremely beneficial. AI can be used to streamline processes in areas such as web marketing, retail product recommendations, and consumer help desks, among other things.
However, while these areas benefit from AI, is Artificial Intelligence the best option for industrial applications such as the Industrial Internet of Things (IIoT), or is there a better option? Many AI methods are self-taught, which eliminates the need for process mapping and other time-consuming analytical processes, making it appear to be a good fit for IIoT. However, only a few methods will be applicable. The most useful methods do not seek impossible amounts of data. They concentrate machine learning in understandable ways. The rest will fail miserably for three reasons.
Complex Systems and their following failures
Production in the twenty-first century is complicated. Each process contains thousands, if not millions, of steps. Each of these varies depending on the equipment, people, and item being processed.
Most AI cannot comprehend such complex processes, but it also fails in simple systems. A simple pump connected to an electric motor, for example, is the least complex machine. This system can fail in 50 different ways, with over 50 different causes of failure. This means that there are 2,500 different ways for this single asset to fail. How long will it take for the AI to gather enough data to diagnose the problem or prevent the impending failure? It could take years, and even then, the AI might not be able to collect all of the data.
If all of the data for training the AI could be collected, the processing time to solve the problem would be lengthy. To get around this, Big Data AI methods employ a technique known as dimensionality reduction. This trick allows the AI to find correlations and combine them into a single variable, simplifying the process. But what happens when there is a problem and the items are not correlated? The AI will not be able to detect what is wrong or prescribe a solution to the problem.
Can artificial intelligence help with complex systems and their failure analysis? Yes, but the most popular brute force methods designed for consumer applications aren’t the solution. In most cases, hybrid AI, which combines machine learning, expert systems, and other techniques, is a better solution.
Operations Evolution
The configuration of capital assets, the workforce, and the nature of production are just three operational processes that production managers are constantly changing. Because Big Data AI is based on long-term observations and statistical correlations, it has difficulty distinguishing between good and bad changes. This is another complex level with Big Data and AI cannot grasp. Thus, we need a focus on hybrid AI which can provide Hyperautomation with a robust platform.
Time-to-Value
Even when traditional AI can be used in manufacturing, it can take a long time to collect data and then learn what to do with it. Waiting months or even years is impractical when solutions are required immediately. Furthermore, the company will have to pay for this service before the AI can prescribe a solution, lowering the return on investment even further.
A hybrid AI approach that combines predictive and prescriptive analytics solutions is more effective than traditional AI for IIoT. The latter has its uses, but it is too slow and expensive for much of the IIoT. Furthermore, traditional AI can be too cryptic when it comes to providing a solution. When AI algorithms have finished training, their original creators are frequently unable to explain how the AI generates its answers. Human decision makers are often hesitant to accept the AI’s conclusions because they do not understand the process.
The best analytics firms, on the other hand, break down large questions into smaller, easier-to-answer questions. This results in solutions that are simple to grasp. Because the interpretation of these predictions is simple and straightforward, humans are more likely to act and act faster.
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