Here's How Siemens Is Providing Predictive Maintenance Analytics to Manufacturers on Its MindSphere IoT Operating System with Senseye

Brought to you by WBR Insights

When it comes to high-value essential components in manufacturing processes, maintenance needs to be proactive rather than reactive. However, the problem is that manufacturing firms face visibility challenges in terms of wear and tear on tools used in precision processes that can lead to higher failure rates.

Improving visibility into machine performance, faults, and tool deterioration is key to moving away from reactive and time-based maintenance and towards a predictive maintenance model, where maintenance engineers are provided with the data they need to be proactive - fixing or replacing assets before they fail. Taking a proactive stance to machine tool maintenance helps to ensure manufactured components are not being built below specifications due to tool degradation, while production uptime and productivity are simultaneously improved thanks to more effective maintenance.

Since its launch in 2016, Siemens' MindSphere - an open cloud IoT operating system - has been helping manufacturers connect their machinery and tools to the cloud, making operational and performance data accessible through digital applications (known as "MindApps") and allowing industrial customers to make maintenance decisions based on factual, real-time information.

"We've seen tremendous growth - especially in the last twelve months," said Siemens' MindSphere VP of Strategy, Christopher Inauen, in a recent interview with IoT World Today. "Some of our 1,100 customers are internal, coming from other business units such as mobility or in building technologies and so forth. But the majority are external. [...] Then we have connected 41 Siemens plants to MindSPhere. We manufacture a lot of things. And in these plants, we are doing condition monitoring, predictive analytics and so forth. Overall, we have connected approximately 1.3 million devices and assets. We have over 250 applications in our portfolio."

This year, Siemens integrated predictive maintenance software Senseye into MindSphere, giving industrial customers even greater insight into the performance and degradation data of their machine tools with enhanced, real-time analytics.

Senseye

Senseye is an award-winning software that leverages machine learning to automate monitoring and forecasting of the condition of machines and plants. The software has been available to MindSphere users since June 2018, when it was offered as a supplementary service that could be connected to the MindSphere operating system. Now, a new version of Senseye's application has been developed especially for the MindSphere environment, hosted within the operating system itself.

Senseye is able to receive and analyze data from machines connected to MindSphere, enabling manufacturers to understand the health of their industrial assets and schedule maintenance activities more accurately, without the need to invest in any additional sensors, applications, specialist staff, or training.

The development makes it quicker and easier for MindSphere users to adopt Senseye's automated predictive maintenance product in their plants. It also means that the software can produce even more precise analyses, as it now has access to historical data.

"Our partnership with Siemens is fantastic for MindSphere users, enabling them to improve the effectiveness and efficiency of their maintenance activities by analyzing data already captured by all modern Industry 4.0 assets," said CEO of Senseye, Simon Kampa. "Our vision for our partnership with Siemens is to provide all MindSphere users with immediate insight into the current and future health of their entire asset base at the touch of a button, with an ROI of less than three months."

With Senseye, run-time analytics give maintenance engineers the visibility they need to perform proactive maintenance. This is enabled by the software's ability to analyze and evaluate data that modern industrial plants already regularly record in a standardized manner. Direct integration into the MindSphere environment provides users with immediate information into machine tools' current and future conditions, allowing organizations to start gaining new insight into shop-floor machinery and start taking the right actions to improve the efficiency and effectiveness of their maintenance operations.

"By providing users with the opportunity to access Senseye as a native app in the MindSphere operating environment, we have reduced the manual processes involved in connecting data to the Senseye product," said Kampa. "We are making it even quicker and easier for engineers to understand the health of their industrial assets and see precisely when those assets are most likely to fail, without any additional sensor or application investment."

Final Thoughts

Predictive maintenance is a hugely valuable practice for building a comprehensive maintenance management program for an industrial plant. While traditional programs rely on either time-based servicing routines or emergency responses to unexpected failures, predictive maintenance schedules specific maintenance tasks only when they are actually needed - minimizing the frequency and cost of unscheduled downtime while improving the overall availability of equipment in operating plants.

With Senseye now fully integrated into MindSphere, more users of Siemens' platform will be able to automatically asses the condition and remaining useful life of their industrial machines, achieve reductions in maintenance costs, and reduce unplanned downtime.

"Senseye is capable of delivering tremendous value to manufacturers and other industrial organizations through its predictive maintenance application," said Paul Kaeley, President of IoT Consulting and Solutions at Siemens. "Senseye provides a compelling solution to MindSphere users who want to take full advantage of the industrial IoT data at their disposal and boost the insight available to their maintenance operations."

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