Senseye Predictive Maintenance

What it does

Senseye Predictive Maintenance takes machine condition data such as vibration, current, torque and temperature from customers' data collection systems, historians, IoT platforms or databases. Its machine learning analysis produces asset health insights, failure risk alerts and prioritized maintenance actions, delivered through a web app.

Maintenance teams use it to see which assets need attention first, then work through issues inside the platform by @mentioning colleagues, recording responses and following a structured workflow to find root causes. Notes and expert knowledge captured in messages are stored and surfaced back by generative AI as historical context. Siemens describes it as a solution approach that can include software, services and expert guidance, complementing existing CMMS and historian systems rather than replacing them.

It suits plant owners and maintenance teams in manufacturing, including automotive, process and discrete manufacturing, that operate multiple assets or sites. Access is through a browser with an internet connection and a company email address for notifications. Optional onboarding into IT/OT systems is a separately contracted paid service. Pricing is obtained by contacting Siemens.

What it can do

• Forecasts machine failure and remaining useful life from machine data
• Prioritizes asset risks across plants, sites and asset types
• Captures maintenance knowledge, notes and messages in a shared platform
• @mentions colleagues and tracks responses within root-cause workflows
• Surfaces historical context from captured expert knowledge using generative AI
• Supports gradual adoption from priority assets to enterprise-wide deployment

Works with

• Historians
• IoT platforms
• Databases
• Existing CMMS and operational systems (complementary, per FAQ)

Example workflows

Prioritizing at-risk assets across a plant

Input: machine data (vibration, current, torque, temperature) from historians or IoT platforms. Steps: the system analyzes the data, flags potential failures and ranks asset risks. Output: a prioritized list of assets with alerts and insights in the web app, so teams decide where to act first.

Recording a root-cause investigation

Input: an alert on a specific asset. Steps: users open the alert in the web app, @mention colleagues, record their responses and follow the structured workflow. Output: a documented root-cause trail with notes that is stored and can be surfaced as historical context later.

Before you choose

• Pricing is not published; obtain a quote from Siemens before planning budget.
• Value depends on having suitable machine measurements; no new hardware is strictly required, though sensors may be added.
• Private email addresses are not permitted for notifications, which may affect user setup.

Listed under

• Operations
• Asset health
• Automotive
• Downtime reduction
• Industrial AI
• Maintenance teams
• Manufacturing

Get started

Contact Siemens for sales and pricing; a 'Request quote' route is available on the Cloud Application page.

1. Connect machine data sources such as historians, IoT platforms, databases or sensors, using the data formats documented by Siemens.
2. Confirm prerequisites: an HTML5-capable browser, a functional internet connection and a company email address for notifications.
3. Optionally engage Siemens for onboarding into your IT/OT landscape, under separately agreed terms and fees.

Contact Siemens for sales and pricing. Optional onboarding is billed on a time-and-material basis under separately agreed terms.

We couldn’t find published prices for Senseye Predictive Maintenance. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Senseye Predictive Maintenance | Siemens ↗www.siemens.com
• Predictive Maintenance at Scale | Senseye Cloud Application | Siemens ↗www.siemens.com
• Senseye Predictive Maintenance ↗cache.industry.siemens.com

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Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.