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How AI Revolutionizes the Industrial Sector

Breakneck speed, unprecedented development and unhindered feasibility are just some of the phrases attached to the spread of Artificial Intelligence across various industries. AI today is at an important junction with huge potential for market growth. Currently valued at around $3 trillion, the market is expected to grow some 250 percent over the next five years to $8 trillion by 2023.

While experts believe that AI is still in a nascent stage, it has achieved widespread success, particularly in industry. As the technology matures, expect to  see more coherent cases of the benefits it brings within specific industries.

However, this doesn’t take anything away from the use-cases or examples already in-hand today. The use of AI in the industrial sector has already started, and it has reaped genuine rewards. The current use of AI offers a good indication of what we can expect in the future. We have a roadmap right in front of us, based on current examples.

Impact on All Industries

AI is being applied in almost every industry/industrial sector. User-based services including Pinterest use deep learning to recognize images and create unique user experiences. Research and development industries use deep learning methods to detect all kinds of security risks on the Internet. Financial companies such as PayPal are assisted by graphical-driven deep learning to catch and detect fraud. Add the convenience of AI to manufacturing, medicine, education, and healthcare, and you get well-rounded technology that hints  of major growth in the future.

AI’s application across industries has been assisted by its combination with other technologies including IoT, cloud computing, augmented reality and big data. All of these technologies have worked together to create the correct infrastructure for AI to operate within.

Based on its uses across industry, AI is considered to create excellent value across a variety of factors. Not only is it expected to accurately forecast and regulate demand, but it will also help companies get the most out of their machines, while putting an end to uncalled-for maintenance and downtime.

These benefits will eventually add up to deliver the preferred customer experience. In the retail industry, for example, AI can help retailers uncover what customers want, sometimes before customers even know it themselves. The possibilities are endless when it comes to imagining all that AI has to offer for industries across the globe.

Transformation of the Industrial Sector

AI has opened new horizons in the industrial sector and has augmented numerous processes and routines.

To start with, AI can be applied across various manufacturing processes. From self-adaptive manufacturing to predictive maintenance, automatic quality control and driverless vehicles, AI acts as the brain behind all of these processes. It can also be used to optimize the production process in a way that reduces inefficiencies and cuts downtime. Industries can also adjust and optimize the parameters within the process.

AI makes it relatively easy for organizations to design and look over the production of new products. It mitigates the risk of launching new products/technologies on the market. Finally, AI can help organizations reach  the source of problems easily. Newer and better anomaly detection methods highlight the source of any problem.

How AI Works

Obviously, achieving the benefits of AI  mentioned above is easier said than done. The models for AI technology take a lot of insight to deliver and can only be achieved through proper analysis and data gathering. AI can work efficiently in several applications to augment industrial processes.  

Predictive Maintenance

Predictive maintenance works towards anomaly detection inside the industry. By using 100 percent of the data being generated in real time, a predictive maintenance model helps to find 80 percent more anomalies.

It has been predicted that more than 40 percent of all unexpected downtimes in businesses are due to asset failure. Moreover, 50 percent more costs are incurred due to fixed assets that have problems that are not uncovered in time. These problems can be solved through the use of cognitive anomaly detection. An anomaly detection method based on AI will use a bottom-up approach to detect possible faults, and then work on them. Once anomalies are detected and predictive maintenance done, organizations will be able to avert the risks of running undue costs and downtime for repairing flawed assets.

Edge Analytics

Edge analytics fine tunes the predictive maintenance process and adds real-time automation to it. Using edge analytics, data is recorded and interpreted in a matter of seconds at the edge, and results generated in real time. This reduces the cost of transferring data across multiple source points, as edge does the work close to the machine. The use of edge in anomaly detection can highlight anomalies in real time before they go on to affect performance in any way.

Visual Inspection

AI can use visual methods to compare products and decide whether they pass inspection. Machine vision in precision quality analysis combines the input from cameras multiple times more sensitive than the human eye, with the AI technology to improve image inference capabilities.

Machine vision tools can seemingly work magic to understand microscopic faults in places that would otherwise go unnoticed. A fault in a circuit board would often go unnoticed, but with video data and machine vision tools, such faults can be detected and worked upon. The machine-learning algorithms are properly trained and supervised to generate actionable insights.

More Efficient Design and Management

The concept of the digital twin has further augmented the use of AI in design generation and anomaly detection. An asset that co-exists with its digital twin can easily be monitored. When a jet engine is affected and starts to degrade or age, its digital twin will start showing these signs of degradation for engineers to monitor easily. This would save future costs and maintenance charges.

Use Cases

There are numerous use-cases of AI in industry , including:

  • The use of the digital twins across numerous industries has resulted in better monitoring of assets. Many airline companies use these digital twins to measure the effects of the environment on their machinery. The digital twin quantifies it through effective imagery.
  • Edge analysis is in place across multiple organizations. Edgification aids the correct utilization of real time data for real time results. Schindler Elevators has used the edge to generate real time data about the performance of its elevators, including metrics like the time taken for doors to close, etc.
  • At the recently-held Cebit, Huawei also provided insights into real-time examples of the use of AI in industries. Smart manufacturing methods help organizations limit their waste and increase production capabilities.
  • Cognitive anomaly detection has been implemented by many organizations across the manufacturing arena. The major premise behind the implementation is to limit downtime which is due to asset or machine failure.

How to Jump onto the AI bandwagon

There are certain requirements that need to be fulfilled to join the AI bandwagon:

Start by building an industrial innovation platform that creates a mix of new technologies, including cloud computing, AI and IoT. Collaborate with the right service providers, devices, and communications to get the desired results. The collaboration between product, data analysis, machine learning, and AR combines to create a simple data model.

Additionally, building partnerships and creating ecosystems for your model is extremely important. No single enterprise can independently cater to your end-to-end solutions. These solutions cover the cloud, terminal connection, application services, and data analysis. You need a partnership with multiple service providers to reach this ecosystem. The aim should be to shift from product first to service first. The industrial innovation platform gives enterprises the drive to shift from product sales to service sales.

By Ronald van Loon

Ronald van Loon

Ronald has been recognized as one of the top 10 Global Big Data, IoT, Data Science, Predictive Analytics, Business Intelligence Influencer by Onalytica, Data Science Central, Klout, Dataconomy, is author for leading Big Data sites like The Economist, Datafloq and Data Science Central.

Ronald has recently joined the CloudTweaks syndication influencer program. You will now be able to read many of Ronald's syndicated articles here.

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