ARTIFICIAL INTELLIGENCE

Time-based monitoring (TBM) has long been the standard for inspecting and repairing manufacturing equipment. But as digital transformation in manufacturing continues to dominate factories, this traditional approach falls short. TBM overlooks early equipment anomalies, leading to unexpected downtime and costly malfunctions. Compounding the issue, TBM depends on highly skilled personnel—an increasingly scarce resource.

“As one of the key goals for digital transformation, business leaders are looking for ways to automate processes. To do so, it is necessary to detect any abnormalities in the factory machines on behalf of on-site workers,” says Daisuke Nishimura, Managing Director at Macnica, an IoT solution aggregator.

To remain competitive and avoid unexpected machine failures, manufacturers are shifting from TBM to industrial predictive maintenance. This shift relies on condition-based monitoring (CBM), an approach that uses sensor technology and AI to deliver smarter, more-proactive results.

A Case for Condition-Based Monitoring

One Japanese-based aerospace manufacturer realized the benefits of condition-based monitoring when it began automating its assembly lines. The manufacturer discovered its perforation process relied too heavily on highly experienced workers, making it difficult to automate. The company set out on a mission to solve this issue using sensors and AI.

“To prevent fuel leaks on aircrafts, there are strict requirements on the drilled-hole quality,” says Nishimura. “If the hole doesn’t meet these criteria, the fix would be costly and require a significant amount of rework.” This meant that any abnormality of the perforating machines must not go undetected.

The goal was to detect machine failures using vibration sensor data. The manufacturer turned to Macnica to help implement the sensors and collect condition data.

Condition-based monitoring is just one of the first steps to an autonomous #manufacturing future where innovations like edge #AI and #ComputerVision truly enable the #SmartFactory. @macnica_inc via @insightdottech

Macnica introduced the SENSPIDER Smart Sensor Gateway, an innovative solution that gathers sensor data at the edge and uploads it to the cloud. This was able to provide the advanced condition-based monitoring services the customer was looking for.

“SENSPIDER can help system integrators work with customers on a wide range of devices and problems,” says Nishimura. “We do this by flexibly supporting optimal sensor deployment and preprocessing for each use case within a single box.”

Now the team can detect equipment failures in real time, and is actively working on integrating more CBM features into their machines. These advancements will help the customer reduce its dependency on highly skilled workers and move closer to achieving full assembly line automation.

Condition-Based Monitoring Unlocks New Industrial Innovation

“The impact of CBM is significant in production line automation, as equipment malfunctions can go unnoticed and lead to a pile of defective products,” says Nishimura. CBM also leads to new opportunities for systems integrators (SIs) and machine builders.

SIs can transform their business model by launching new features and services that leverage CBM. For example, they can remotely monitor equipment conditions and perform optimal maintenance for their customers. And they can provide advanced services with automated, AI-based alarms that notify if there are signs of abnormalities.

Machine builders looking to offer as-a-service solutions can also benefit from these capabilities.

“The growing market demand for CBM has become a major factor that motivates machinery manufacturers to develop CBM as an added value,” Nishimura says. “It is difficult to expect dramatic improvements in individual machine performance. More and more manufacturers are starting to focus on improving services as a new differentiating factor.”

Industrial Predictive Maintenance in Action

Implementing CBM is not without its challenges. Manufacturers can struggle to move beyond proof-of-concept (PoC) to production if they lack a clear goal

“It is important to determine whether the system is worth building and valuable for users by considering the cost of maintenance, the cost impact of equipment failure, and the benefits of improved productivity and quality before proceeding with the project,” Nishimura says.

Success also depends on buy-in from upper management, who can allocate the appropriate budget and resources to get projects off the ground. “This kind of problem often occurs if the project is not aligned with the company’s strategic plans and policies, or if it is not recognized as a worthwhile initiative,” Nishimura explains.

Other technical challenges of condition-based monitoring include choosing the right sensors, managing hardware costs for mass production and operation, and acquiring effective training data.

With solutions like SENSPIDER, SIs can incorporate customized functions, integrate with cloud environments, and accelerate development and deployment. And by leveraging Intel^® SoC technology, Macnica can provide a high-performance platform at a lower cost than other solutions.

The company believes a five-phase approach is the best path to CBM development: building the sensing environment, performing simple data analysis, model iteration, PoC, and productization.

“We recommend beginning with identifying the target machine type, building the sensor environment, and collecting the preliminary sensor data. This will help you run a quick initial iteration of data analysis,” says Nishimura.

The Future of Digital Transformation in Manufacturing

Condition-based monitoring is just one of the first steps to an autonomous manufacturing future where innovations like edge AI and computer vision truly enable the smart factory. In addition to remote monitoring and industrial predictive maintenance, the latest technologies can automatically tune equipment to best suit operating conditions.

Preventing machine defects and degradation clearly helps manufacturers lower costs and improve margins. Machine builders and SIs win, too. With as-a-service offerings, they create a more sustainable and profitable business model.

This article was edited by Georganne Benesch, Editorial Director for insight.tech.

This article was originally published on October 1, 2021