The modern factory is equipped with tons of data-generating machines, sensors, production, and connected equipment. Yet, data generation alone does not guarantee improved performance. Rather, the task is to convert raw data into intelligent information used for decision-making.
That's where industrial AI and IoT come in handy.
From Machines to Intelligence
The connected industrial environment is structured as:
Machines → Sensors → Data → Analytics → AI → Insights → Action
The sensors record temperatures, vibrations, energy consumption, machine status, and other parameters. IoT system takes this data and transmits it for further storage, processing, and analysis. AI detects patterns, anomalies, and relationships not visible within separate metrics.
The task is not in data collection, but in understanding their meaning from the point of view of operations.
Applications
Industrial AI provides predictive maintenance through analysis of vibration, temperatures, pressures, electric currents, and maintenance history. This way, people can investigate potential equipment failure without the need for an unplanned shutdown.
Industrial AI increases the efficiency of quality control. Analyzing process conditions and production outcomes, smart systems will identify the abnormalities indicating potential issues with the process.
Connected systems increase the visibility of assets and inventories as they show the current location of these resources, ways of using them, and their availability.
The Main Challenge Is Data
The major obstacle is always in the quality of the data rather than in the AI algorithm. The problem may lie in the lack of connectivity in legacy machines, data formatting incompatibility, sensors' inability to collect enough clean data. Thus, successful project needs solid integration, infrastructure, connectivity, and security of information.
Human Expertise Is Critical Too
Industrial AI has to complement, not substitute, the operational knowledge of people. The experienced operator knows the context of abnormal values, combining them with the results of the analytics, he/she can make them more reliable and actionable.
Start With the Problem
One should start with a specific problem like downtime reduction, quality improvement, increase of asset visibility, bottleneck detection. With clear outcome and clean data, one can implement industrial AI and IoT successfully. Learn more at Aperture Venture Studio.
Top comments (0)