Siemens expands Industrial Copilot with New generative AI-powered

西门子通过新的生成式人工智能扩展了Industrial Copilot

Maintenance Offering (Source: Siemens AG)

维护服务（来源：西门子股份公司）

The Siemens Industrial Copilot is revolutionizing industry by enabling customers to leverage generative AI across the entire value chain – from design and planning to engineering, operations, and services. For example, the generative AI-powered assistant empowers engineering teams to generate code for programmable logic controllers using their native language, speeding-up SCL code generation by an estimated 60% while minimizing errors and reducing the need for specialized knowledge.

西门子工业助手通过让客户在整个价值链中利用生成式人工智能，从而彻底改变行业——从设计和规划到工程、运营和服务。例如，这个由生成式人工智能驱动的助手能够帮助工程团队使用他们的母语生成可编程逻辑控制器的代码，估计可将SCL代码生成速度提高60%，同时最大限度地减少错误并降低对专业知识的需求。

This in turn reduces development time and boosts quality and productivity over the long term..

这反过来减少了开发时间，并在长期内提高了质量和生产力。

Siemens is developing a full suite of copilots to industrial-grade standards for the discrete and process manufacturing industries – and is now strengthening its Industrial Copilot offerings with the launch of an advanced maintenance solution, designed to redefine industrial maintenance strategies.

西门子正在为离散和流程制造行业开发一套符合工业级标准的完整副驾工具套件，并通过推出一种先进的维护解决方案，进一步加强其工业副驾产品系列，旨在重新定义工业维护策略。

Siemens brings generative AI to the entire maintenance cycle

西门子将生成式人工智能引入整个维护周期

The new generative AI-powered solution will support every stage of the maintenance cycle, by helping industries move beyond traditional maintenance practices toward an intelligent, data-driven approach. To realize this, the Senseye Predictive Maintenance solution powered by Microsoft Azure will be extended with two new offerings:.

这一全新的生成式人工智能解决方案将支持维护周期的每个阶段，帮助各行业超越传统的维护实践，迈向智能化、数据驱动的方法。为此，由微软Azure驱动的Senseye预测性维护解决方案将新增两项服务：

Entry Package:

入口包：

This solution provides an accessible and cost-effective introduction to predictive maintenance, combining AI-powered repair guidance with basic predictive capabilities. It helps businesses transition from reactive to condition-based maintenance by offering limited connectivity for sensor data collection and real-time condition monitoring.

该解决方案提供了易于获取且具有成本效益的预测性维护入门方法，将人工智能驱动的维修指导与基本的预测功能相结合。它通过提供有限的传感器数据收集和实时状态监控连接，帮助企业从被动维护转向基于状态的维护。

With AI-assisted troubleshooting and minimal infrastructure requirements, companies can reduce downtime, improve maintenance efficiency, and lay the foundation for full predictive maintenance..

借助人工智能辅助的故障排除和最少的基础设施要求，公司可以减少停机时间、提高维护效率，并为全面的预测性维护奠定基础。

Scale Package:

缩放包：

Designed for enterprises looking to fully transform their maintenance strategy, this package integrates Senseye Predictive Maintenance with the full Maintenance Copilot functionality. It enables customers to predict failures before they happen, maximize uptime, and reduce costs with AI-driven insights.

该套餐专为希望全面转型其维护策略的企业设计，集成了 Senseye 预测性维护和完整的维护助手功能。它使客户能够在故障发生前预测问题、最大化正常运行时间，并通过人工智能驱动的洞察降低维护成本。

Offering enterprise-wide scalability, automated diagnostics, and sustainable business outcomes, this solution helps companies move beyond traditional maintenance, optimizing operations across multiple sites while supporting long-term efficiency and resilience..

提供企业级可扩展性、自动诊断和可持续的业务成果，该解决方案帮助企业超越传统维护，优化多个站点的运营，同时支持长期效率和弹性。

The new offering enables comprehensive

新的产品提供了全面的

coverage of the entire maintenance cycle – from reactive repair to predictive

覆盖整个维护周期——从被动维修到预测性维护

and preventive strategies – by leveraging generative AI-driven insights that enhance

并通过利用生成式人工智能驱动的洞察来增强预防策略

decision-making and efficiency across industrial environments.

决策效率以及各种工业环境中的效率。

As industries increasingly seek ways to

随着各行业越来越多地寻求方法来

enhance reliability and reduce costs, maintenance operations are evolving from

增强可靠性并降低成本，维护操作正在从

reactive to proactive approaches. Traditional maintenance strategies often lead

从被动到主动的方法。传统的维护策略常常导致

to costly downtime and other inefficiencies. Siemens addresses this challenge

导致昂贵的停机时间和其它效率低下的问题。西门子针对这一挑战采取措施应对。

by integrating AI-driven maintenance solutions that help companies optimize their

通过整合人工智能驱动的维护解决方案，帮助公司优化他们的

asset performance and maximize operational uptime. The fusion of generative AI

资产绩效并最大化运营正常运行时间。生成式人工智能的融合

and predictive maintenance allows customers harness real-time data and advanced

预测性维护使客户能够利用实时数据和先进

analytics that ensure timely interventions and strategic planning.

确保及时干预和战略规划的分析。

First pilot use cases have shown that the Industrial

首批试点用例表明，工业

Copilot for maintenance helps save on average 25% reactive maintenance time.

维护副驾平均可节省 25% 的被动维护时间。

This

这

expansion of our Industrial Copilot marks a significant step in our mission to

我们的工业副驾的扩展标志着我们使命中的重要一步

transform maintenance operations,” said Margherita Adragna, CEO Customer

转变维护操作，”客户首席执行官玛格丽塔·阿德拉格纳表示

Services at Siemens Digital Industries. “By extending our predictive

西门子数字工业公司的服务。“通过扩展我们的预测性维护

maintenance solutions, we’re enabling industries to seamlessly shift from

维护解决方案，我们正在使各行业能够无缝过渡到

reactive to proactive maintenance strategies and drive efficiency and

从被动维护策略转向主动维护策略，提高效率并推动

resilience in an increasingly complex industrial landscape.”

“在日益复杂的工业环境中具备韧性。”

With this innovation, Siemens continues to

通过这一创新，西门子继续

advance its vision of a digitalized industry, by providing customers with an

通过为客户提供一个数字化的行业愿景，来推进其发展，

intelligent and integrated approach to maintenance that ensures long-term

智能且集成的维护方法，确保长期有效

operational success.

操作成功。