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Why Senseye Cloud Application?

Senseye Cloud Application is cloud-based predictive maintenance software that helps manufacturers reduce downtime, cut maintenance costs and scale asset intelligence across plants.

Using advanced AI combined with human expertise, Senseye automatically forecasts machine failure and prioritizes risks, so teams know where to act first. No data science expertise or manual analysis is required.

Senseye Cloud works with your existing data from legacy machines, historians, IoT platforms or new sensors. It integrates seamlessly across assets, systems and sites, enabling a consistent predictive maintenance approach that grows with your operations.

Key benefits

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Capture and share maintenance knowledge

Preserve expert knowledge and make it accessible across teams and sites by capturing machine behavior, failure patterns and maintenance insights in a single, shared platform.

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Reduce unplanned machine downtime

Detect early signs of failure across assets and sites to prevent unexpected stoppages and production losses.

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Optimize maintenance costs and resource use

Move from reactive to predictive maintenance to reduce costs, extend asset life and deploy teams where they add most value.

Featured capabilities

Discover how others use Senseye Cloud Application

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Case study

Predictive maintenance across global steel plant

Company:BlueScope Steel

Siemens Software:Senseye Cloud Application

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Senseye Cloud Application in Automotive

AI-driven predictive maintenance helps automotive manufacturers reduce downtime, stabilize production and maintain asset health across high-volume, multi-site operations.

Frequently asked questions

Explore resources

Podcasts

Podcast | BlueScope: scaling predictive maintenance

Podcast | The role of a Predictive Maintenance Champion

Podcast | How to start a predictive maintenance project

Podcast | The 5 phases of a successful PdM deployment

Whitepapers and reports

E-book | Enabling PdM through Industrial Connectivity

Whitepaper | Maximising your ROI with scalable PdM

Guide | Implementing PdM at scale

Report | The transformative role of Generative AI in PdM

How-to | Proof of Concept

References and Use Cases

Reference | Siemens and Sachsenmilch break new ground in AI-driven PdM

Reference | Advanced machine monitoring for Mercer Celgar

Use Case | Automotive manufacturer

Use Case | Food & Beverage

Use Case | Aluminium manufacturer

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