Predictive Maintenance Ensures Smooth Manufacturing Operations

With increasing reliance on automated manufacturing operations, predictive maintenance solutions allow operators to stay ahead of critical equipment failures. AI-based solutions monitor even the most minute changes, so operators can schedule downtime with the least impact on productivity.

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Image credit: Senseye/Nissan Global factory                                                                                 

Automation is a critical component of manufacturing. Product demand is high, but sensitive machines and worker shortages can delay production. The adoption of Internet of Things and edge computing technologies are improving manufacturing processes and productivity.

According to Fortune Business Insights, the industrial automation market will climb from $191.89 billion in 2021 to $395.09 billion by 2029. Automation is about simplifying manufacturing operations and improving business processes. With enhancements in digital automation comes the need for predictive maintenance software.

Senseye is a global player in this market. The company, recently acquired by Siemens, developed Senseye PdM, a cloud-based predictive maintenance solution that can support thousands of assets across multiple locations. Using artificial intelligence and machine learning, Senseye PdM monitors machinery, environmental conditions, and situational factors to predict equipment failures before they happen.

Reduce the Cost of Downtime

Unplanned downtime can cripple manufacturing operations and can cost companies hundreds of thousands of dollars. Identifying the problem after the fact, then locating and installing parts takes time and leaves maintenance teams scrambling to fix failed machinery quickly to avoid a prolonged disruption on the line.

Senseye PdM can be integrated on any kind of device, system, or data source, providing remote access to operators. Through AI and machine learning, Senseye PdM discovers how those machines and systems best work, through real-time monitoring of factors such as vibration, noise, temperature, and usage.

It also considers user behavior. That level of automated scrutiny enables Senseye to predict failures three to six months in advance, providing maintenance staff ample time to plan for outages and disruptions and ensure consistent operations.

“It helps the maintenance teams work more efficiently,” says Niall Sullivan, Global Marketing Manager at Senseye. “The maintenance team could be monitoring thousands of machines but only be alerted to the five machines that have an issue. Senseye does that automatically.”

In fact, maintenance staff should only need to log in to the system for about 15 minutes every morning, Sullivan says. Issues are highlighted on the dashboard, and staff can respond remotely. Senseye PdM also tracks operator feedback, so the system learns which anomalies are less critical: Did a catastrophic machine failure occur on Line 4, or did someone forget to turn on the system?

Worker using Senseye technology

Image credit: Senseye

The Case for Predictive Maintenance

The Senseye solution can be installed on existing infrastructure. Operators can use a desktop or mobile device to remotely access global assets, view monitoring data, and run reports on maintenance activity. In Industry 4.0 environments, the Senseye PdM deployment is nearly immediate, and insightful machine diagnostics are presented within two weeks. The return on investment is realized in about three months, and Senseye stands behind it, Sullivan says.

“We offer a guaranteed ROI,” he says. “If our software fails to spot an issue that leads to unplanned downtime, we’ll refund the licensing fee.”

Senseye teamed with Intel® for scalable edge computing capabilities, with Microsoft for Azure cloud services, and with Arrow Intelligent Solutions. Arrow supplies the products and services to design, integrate, and support the Senseye PdM solution.

“Our value proposition is that we help them scale worldwide,” says Roland Ducote, Arrow. “We support Senseye through system integrators. That’s what we like to do.”

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