Senseye
UnclaimedCloud-based predictive maintenance software for manufacturers to reduce downtime and optimize costs.
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TL;DR - Senseye
- AI-driven predictive maintenance for manufacturing assets.
- Reduces unplanned downtime and optimizes maintenance costs.
- Works with existing data sources and scales across multiple sites.
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Eliminates the need for data science expertise for predictive maintenance
- Utilizes existing data infrastructure, avoiding new hardware investments
- Provides rapid value and actionable insights after onboarding
- Supports consistent predictive maintenance across large, multi-site operations
- Reduces unplanned downtime and extends asset life
Cons
- Requires integration with existing data sources, which may involve initial setup effort
- Specific pricing details are not publicly available
Key Features
AI-driven asset intelligence for risk and remaining useful life forecastingCompatibility with any asset and data source (historians, IoT platforms, databases, sensors)Scalable deployment across thousands of assets and multiple sitesAutomated machine failure forecasting and risk prioritizationCapture and sharing of maintenance knowledge and failure patternsStandardized insights, prioritization, and workflows
Pricing
Paid
Senseye offers paid plans. Visit their website for current pricing details.
About Senseye
By Toolradar Team·
Senseye Cloud Application is a cloud-based predictive maintenance solution designed for manufacturers. It leverages advanced AI combined with human expertise to automatically forecast machine failure and prioritize risks across plants. This allows maintenance teams to proactively address issues, significantly reducing unplanned downtime and optimizing maintenance costs.
The software integrates seamlessly with existing data sources such as legacy machines, historians, IoT platforms, and new sensors, eliminating the need for new hardware. It is built to scale, providing consistent predictive maintenance across thousands of assets and multiple sites without requiring data science expertise or manual analysis. Senseye Cloud Application is suitable for various industries including automotive, process industries, and discrete manufacturing, helping stabilize production, maintain asset health, and improve reliability.
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Senseye FAQ
Does Senseye Cloud Application require the installation of new sensors or hardware to function?
No, Senseye Cloud Application is designed to utilize your existing machine condition and operational data from sources like historians, IoT platforms, or databases. While new sensors can be added if desired, they are not mandatory to begin using the platform.
How quickly can a manufacturing operation expect to see actionable insights after implementing Senseye Cloud Application?
Senseye Cloud Application is engineered for rapid value delivery. Customers typically begin gaining actionable insights soon after their assets are onboarded and data sources are connected, due to its automated analysis and prioritization capabilities.
Can Senseye Cloud Application effectively manage predictive maintenance for a diverse range of machines and production lines within discrete manufacturing environments?
Yes, Senseye Cloud Application is specifically designed to improve reliability across diverse machines and production lines in discrete manufacturing. It optimizes maintenance resources and spare parts strategies by providing consistent insights and prioritization.
What kind of data sources can Senseye Cloud Application integrate with to perform its predictive analysis?
Senseye Cloud Application can connect to a wide array of existing data sources, including historians, various IoT platforms, databases, and both legacy and modern sensors. It is built to work with the data you already collect.
Is there support available to help organizations implement and scale Senseye Cloud Application across their operations?
Yes, Siemens provides expert support and guidance to assist organizations throughout the entire process, from initial implementation to scaling and optimizing predictive maintenance strategies using Senseye Cloud Application.
Source: senseye.io