Siemens Leverages Generative AI to Transform Predictive Maintenance

Mumbai, India – Siemens AG, a multinational technology conglomerate, is releasing a new generative artificial intelligence (AI) functionality into its predictive maintenance solution – Senseye Predictive Maintenance. This advance makes predictive maintenance more conversational and intuitive. Through this new release of Senseye Predictive Maintenance with generative AI functionality, Siemens will make human-machine interactions and predictive maintenance faster and more efficient by enhancing proven machine learning capabilities with generative AI.

Synergy of generative AI and machine learning

Senseye Predictive Maintenance uses artificial intelligence and machine learning to automatically generate machine and maintenance worker behavior models to direct users’ attention and expertise to where it’s needed most. Building on this proven foundation, now generative AI functionality is being introduced that will help customers bring existing knowledge from all of their machines and systems out and select the right course of action to help boost the efficiency of maintenance workers.

Currently, machine and maintenance data are analyzed by machine learning algorithms, and the platform presents notifications to users within static, self-contained cases. With little configuration, the conversational user interface (UI) in Senseye Predictive Maintenance will bring a new level of flexibility and collaboration to the table. It facilitates a conversation between the user, AI, and maintenance experts: This interactive dialogue streamlines the decision-making process, making it more efficient and effective.

From predictive maintenance to prescriptive maintenance

In the app, generative AI can scan and group cases, even in multiple languages, and seek similar past cases and their solutions to provide context for current issues. It’s also capable of processing data from different maintenance software. For added security, all information is processed within a private cloud environment, safeguarded against external access. Additionally, this data will not be used to train any external generative AI. Data doesn’t need to be high-quality for the generative AI to turn it into actionable insights: With little to configure, it also factors in concise maintenance protocols and notes on previous cases to help increase internal customer knowledge. By better contextualizing the information at hand, the app is able to derive a prescriptive maintenance strategy.

The new generative AI functionality in the Software-as-a-Service (SaaS) solution Senseye Predictive Maintenance will be available starting this spring for all Senseye users. The combination of generative AI and machine learning creates a robust, comprehensive predictive maintenance solution that leverages the strengths of both.

Driving productivity and digital transformation

For Siemens, introducing generative AI into predictive maintenance isn’t just about enhancing the technology; it’s about driving tangible benefits for manufacturers. By enabling faster and easier maintenance decisions, it increases productivity, promotes sustainability, and accelerates digital transformation across the entire organization. It also addresses skill shortages in the industry, because it captures and resurfaces expert knowledge from the aging workforce. In doing so, it empowers less-experienced shop-floor employees, making them more efficient and effective in their roles.

Image Source: Siemens