If artificial intelligence caused skepticism and distrust among industrial enterprises a few years ago, the emergence of new market requirements, the growth of government influence, and the development of new technologies have radically changed the situation. The issue is what specifically can be credited to the area of artificial intelligence, particularly in production, as the topic is extensive, but at the same time ambiguous from the point of view of the application.
Today, the terms “digitalization”, “machine learning”, and “artificial intelligence” are pronounced from the stands, actively discussed in the media, covered on state and commercial channels, and propagandized in the mass consciousness. Since the terminology has yet to be fully settled, there are often discrepancies that lead to misunderstandings between various parties. Most often it concerns the words “artificial intelligence”, which is understood by everybody in their way.
So, can a machine think the same way as a human? Make decisions? Create new precedents? Does the device have consciousness? And how does the modern industrial sector put into practice the technologies generated by the Fourth Industrial Revolution?
Artificial intelligence in industry – Areas of application
What tasks can be solved using AI in manufacturing industry? Firstly, those that a person can not cope with. This can work in hard-to-reach places, harmful chemical production, permafrost conditions, or increased radiation. Secondly, those where “natural intelligence” is applicable but unproductive are the prediction of critical malfunctions, prevention of unexpected machinery breakdown, maintenance by condition, and forecasting the residual life of the equipment. These are, in fact, those areas where a person can do work, but in states of a vast amount of information, it becomes almost impossible. Moreover, a person can not always correctly sort data and resolve contradictions. And the machine can perform these activities carried out using established algorithms. It is possible to get the necessary amount of data in the industry only with an integrated approach: a combination of a system model based on physical processes and machine learning algorithms. Similar advancements are seen with the implementation of AI in logistics, where optimizing supply chain management and route planning enhances efficiency and reduces costs.
Let’s look at some examples.
Diagnostics of equipment during operation
For industries that are traditionally engaged in the operation of equipment, it is important to have constant feedback from a working product to assess the behavior of an object in real-time, predict possible emergencies, and prevent unexpected machinery breakdown.
In these situations, information must be extracted by analyzing a lot of data from the systematically collected information. But in industry, there is often not enough information received from real-life objects, so the database needs to be supplemented with the results of field and virtual experiments, using engineering analysis technologies based on numerical modeling, and conducting regular calibration to improve the quality of the forecast.
Consequently, we have a model built on historical data and reinforced with information from computer simulations and robots, so there is enough information for high-quality training. This helps explain the trends identified during the analysis, as well as predict the emergence of new aspects and even classify or segment data based on patterns of behavior that are almost impossible to identify using traditional “human” methods.
Optimization of operating modes of the equipment and technical approaches
Reducing unplanned downtime and increasing the service life of the equipment and, as a result, improving product quality and reducing the costs of the enterprise as a whole depends on the correctly selected operating modes of the product. A system that determines the most optimal scenarios of technical approaches and predicts deviations in the operation of equipment based on technical studies and statistical models can help the operator.
Maintenance by condition
The transition to maintenance by condition allows you to increase the service life of the equipment and its inter-repair period, as well as the detection of defects due to data supplied in real-time. Information about the current state of components and assemblies and the forecast of the remaining resource allows us to form recommendations for the maintenance and repair of equipment, to ensure the timely delivery of spare parts. You can determine in advance that something is wrong with the unit and decide on preventive maintenance.
Internal modeling algorithms can also be used here: any complex adaptive system is capable of creating internal environmental models that allow predicting future events and changes for their successful adaptation.
Visual recognition of defects. Computer vision
Machine vision is a set of technologies that allows computers not just to process images as an array of data, but to perceive and interpret them in a human-like way. It is becoming increasingly popular in the industry, as such methods allow you to automate and significantly improve the process, which requires visual control.
Flexible energy management. Improving energy efficiency
Machine learning technologies make it possible to reduce the operating time of equipment in high-intensity mode, eliminate surplus inventory and foresee equipment wear in time and residual resources, reduce waste and also lower the cost of energy use by accounting for the condition of the surrounding environment.
Specialists for AI
One of the most crucial components that are also worth paying attention to for the development of competencies in the field of artificial intelligence is the training of new personnel. More and more specialists of a new type are needed – technical analysts/data scientists (TADS). They are experts who combine both knowledge of the subject area and AI systems and the principles of their development and application. They will be able to connect subject tasks and AI solutions. Their responsibilities include translation from the language of an industrial task into the language of data, metrics, and algorithms, and evaluation and examination of the obtained AI models and systems in terms of their applicability to the task and embeddability in industrial processes.
Conclusion
Equipment downtime is decreased since probable faults and issues are foreseen and warned of early. The ability to remotely monitor equipment and speed up the fault investigation process increases staff productivity, and making repairs by the real state streamlines routine maintenance plans and enables you to boost profitability.
A superintelligence capable of learning as a person, acquiring new knowledge, and solving previously unknown tasks, which would not be inferior in intelligence to most people, and in many things would even surpass it, has not yet been created. Nevertheless, process operators, technological experts, and engineers can use artificial intelligence algorithms as auxiliary adjustment instruments to help them make judgments that will maximize production. And even now, the technologies of the fourth industrial revolution offer modern enterprises to focus on an integrated approach.
This story was provided by Victoria Fischer
