The Internet of Things has changed the game, especially at the industrial level. Industrial IoT analytics can help managers determine which aspects are causing bottlenecks, quality control issues, or accidents. Conversely, they can identify improvements most likely to increase productivity or reduce equipment downtime. However, to get the most out of IoT in an industrial environment, people need to know and deploy the essential technologies that unlock the capabilities of data analysis tools in a busy environment.
Cloud computing
As leaders begin to focus on industrial IoT analytics, they need to determine how to store and access all the data they already have or will soon collect. Fortunately, cloud computing is ideal for meeting these and other needs that may arise once parties begin to seriously analyze data.
A typical manufacturing plant may have hundreds or even thousands of connected assets, each containing data that a decision maker could use to better understand what is happening at any given time. Consider a massive consumer packaged goods company whose executives wanted to increase the use of IoT across several of its global brands. This deployment involved connecting a staggering 2.8 million IoT devices towards a centralized cloud-based platform.
In addition to providing excellent scalability for massive projects like this, cloud computing supports distributed workforces and locations, allowing users to connect data collection devices in multiple locations. As an example, this company’s IIoT efforts involved internet-related products in 97 countries. Additionally, one of the cloud tools chosen by company executives can manage billions of IoT devices and does not require infrastructure management.
READ MORE: Asset management becomes flexible and urgent
Another benefit of including cloud computing in your industrial IoT data analysis plans is that authorized parties can connect from anywhere with Internet access and pull the latest statistics from their tablets, smartphones or computers. This access-anywhere capability supports collaboration between experts in many locations, which can improve product design or processes. In one case, people relied on data analysis to make what was approximately 30% stronger than its counterpartsshowing how a collaborative and focused approach can pay off.
Equipment sensors
Although industry leaders must consider the individual needs of their facilities before implementing IoT, many naturally look to what others have already achieved for inspiration. Many then realize that connecting connected sensors to critical equipment makes good business sense. This allows them to receive alerts on issues that could degrade quality control or cause avoidable asset downtime.
In one example, executives at a conveyor belt company deployed IIoT sensors and a complementary platform allowing customers to engage in continuous monitoring. This decision was made after executives realized that conveyor belts were the most subject to wear and tear and that misalignment or damage to the belts could be significantly disruptive to customers who rely on this conveyor belt moving equipment. materials in their critical operations.
READ MORE: Things to Consider When Selecting an Industrial Photoelectric Sensor
One application involved the use of equipment sensors to monitor the belt blades of conveyors used in the mining industry. The hardware collects data in real time and compares it with historical performance informationallowing the system to report anomalies. Users can then leverage this data to make more appropriate decisions to keep production running smoothly.
The continuous data flow also supports planning by helping managers identify the best times to take machines offline for essential maintenance. The alternative is that machines can break down unexpectedly, leading to wasted time and increased costs.
Additionally, monitoring specific characteristics allows users to create a baseline to establish the overall condition of the equipment and its typical performance. A common option is to perform vibration testing as part of a predictive maintenance strategy. Connected sensors can analyze the intensity or frequency of specific vibrations to identify possible anomalies. However, environmental factors can affect the amount of vibration an object has. Fortunately, sensors can reveal contributing characteristics, such as humidity and temperature of an installationmaking it easier to assess the extent of unusual vibration patterns.
Artificial intelligence
Artificial intelligence has undoubtedly taken industrial IoT analytics to the next level. This improvement has occurred primarily because AI can detect patterns in large amounts of data, allowing users to draw conclusions much more quickly than they could without the technology’s help.
Customer order forms, equipment statistics, social media comments, and computer vision images could all contain clues about how a manufacturing site could improve quality control measures while increasing productivity. overall production and optimizing processes.
However, trying to make sense of all that data manually would likely prove too time-consuming and tedious to be worth the effort. AI algorithms make data processing more efficient, which is ideal for organizations with large and constantly evolving repositories of information.
Many AI applications complement other technologies. For example, it is increasingly common for people to use equipment sensors powered by artificial intelligence. Such hardware can also use edge computing infrastructure to significantly reduce the transfer distance when moving data to the cloud for processing. Some compatible Edge devices even have on-device processing, increasing the security of sensitive data.
Chatbots
Some people have also explored how generative AI could complement these use cases. This is a type of artificial intelligence that goes beyond more traditional cases and allows people to interact with tools while using natural language, as if they were talking to a friend or colleague. Many of the most popular generative AI business tools are chatbots.
In one example, an individual applied information from a customer corrective action request regarding welding rods found without the necessary material lot numbers. They asked a chatbot to ask five questions that the organization could use to determine the root cause of this problem.
READ MORE: Q&A: Upskilling Manufacturing Plant Workers with AI
The tool’s response featured questions formatted to follow the well-known “five whys” technique, which helps solve problems effectively. It involves asking several questions that gradually get closer to the heart of the subject. For example, the first question suggested by the chatbot asked why the welding rods did not have the required number. However, this is the fifth and final reason why the company did not follow a process to ensuring that the appropriate parties add the digital identifiers.
Since the company almost certainly had an established system that broke down at some point to cause this outcome, the chatbot encouraged people to examine what went wrong and why. Policymakers could collect data related to this AI-assisted process to track trends and ensure that missing numbers were outliers and not signs of a larger, previously unrecognized problem.
Additionally, some vendors are developing generative AI products that can answer questions based on companies’ internal data, providing analytical benefits. For example, a user might ask: “How many of our circuit boards have failed quality checks in the last six months?” » This is an emerging example of AI data analysis outside of conventional methods.
Industrial IoT Analytics Requires Supporting Technologies
These examples show why users will achieve the best results with their Industrial IoT analytics efforts by choosing complementary technologies to meet their needs. The above will encourage leaders to consider the possibilities and get excited about how investments in IoT could connect to overall organizational goals.
