This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies. Read our privacy policy>Search

  • banner pc

    How AI Platforms are Revolutionizing Industry

Breakneck speed, unprecedented development and unhindered feasibility are just some phrases attached to the spread of AI across various industrial sectors in general. Artificial Intelligence (AI), as it stands today, is at an important juncture. The current potential for market growth in AI is extremely high. The market, which is currently estimated to be around USD 3 trillion, is expected to grow to USD 8 trillion in the next five years, which would be over 250 percent growth within a period of five years.

The success of AI platforms in general has been replicated by the success of AI in the industrial sector. Experts currently believe that AI is in a nascent stage. Growth will further materialize over time, and we will have more illustrative cases of the benefits that it brings within industries.

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

Impact on All Industries

AI platforms are being applied in almost every industry/industrial sector out there. 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 pattern-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 is hinting toward major growth in the future.

AI’s application across industries has been assisted through its combination with other technologies including the Internet of Things (IoT), cloud computing, Augmented Reality (AR), and big data. All of these technologies are working together to create the correct operating infrastructure for AI.

Based on its uses across the industry, AI creates excellent value across a range of sectors. Not only is it expected to accurately forecast and regulate demand, but it will also help companies to get the most out of their machines, while putting an end to unnecessary maintenance or downtime.

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

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

To start, AI platforms 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. AI can also be used to optimize production processes in ways that reduce inefficiencies and cut downtime. Industries can also adjust and optimize the parameters within the process.

AI makes it relatively easy for organizations to design the production of new products. AI mitigates the risk of launching new products/technologies on the market. Finally, AI can help organizations identify and highlight the sources of problems more easily by using new and better anomaly detection methods.

How AI Works

Obviously, all the benefits of AI that have been mentioned above are 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 toward 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 occur because of asset failure. Moreover, fixed assets with problems that remain undiscovered before failure incur 50 percent greater costs. Cognitive anomaly detection can solve these problems. An AI-based anomaly method detects possible faults using a bottom-up approach, and then works to rectify them. Once anomalies are detected and predictive maintenance completed, organizations avoid the risks, inflated costs, and downtime of repairing failed components.

  • Edge Analytics
  • Edge analytics fine tune the predictive maintenance process with the addition of real-time automation. With analytics data recorded and interpreted within seconds at the edge, results will be generated in near real-time. The cost of transferring data across multiple connection points is reduced, as the processors at the edges perform a first stage of work close to the sources of the information. The use of edge computing for anomaly detection can highlight operational issues in real time before performance is affected 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 that are many times more sensitive than the human eye, with the AI technology used to improve image inference capabilities.

    Machine vision tools work magic to reveal microscopic faults in places that would otherwise go unnoticed. Circuit board faults would often go unnoticed but for the use of video data and machine vision tools. Machine-learning algorithms are rigorously trained and supervised to generate actionable insights so that all such faults are detected and repaired. 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. Assets that coexist with a digital twin are easy to monitor. When a jet engine is affected and starts to degrade or age, its digital twin will show these signs of degradation for engineers to monitor easily. This will save future costs and maintenance charges.

    Use Cases

    There are numerous use-cases of AI in the industrial world, including:

  • The use of the digital twins across numerous industries has resulted in better asset monitoring. Many airline companies use these digital twins to measure the effects of the environment on their machinery. Digital twins quantify results 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 is using edge computing to generate real time performance data for elevators, including metrics like the rate of speed for doors opening and closing.
  • At CEBIT 2018 in Hannover, Germany, Huawei provided insight into real-time examples of the use of AI in industry, including smart manufacturing methods to help organizations limit waste and increase production capabilities.
  • Cognitive anomaly detection has been implemented by many organizations across the manufacturing sector based on the need for resources that will limit downtime due to asset or machine failures.
  • Jumping onto the AI Bandwagon

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

    Start by building an industrial innovation platform that based on a mix of new technologies, including cloud computing, AI, and the 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 in order 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 selling products to providing services.

    In conclusion, it can be agreed that AI platforms are transforming the industrial sector, and will play an important role in the times that are to come for industries across the globe.