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On-Premises Analytics for Industrial Drivetrains
Siemens introduces a local analytics solution for condition monitoring of industrial drive systems, enabling AI-based diagnostics while meeting data sovereignty and low-latency requirements.
www.siemens.com
Siemens has expanded its industrial analytics portfolio with an on-premises solution for monitoring mechanical and electrical drivetrain components. Designed for environments with strict data governance and latency constraints, the system enables continuous condition monitoring using locally executed AI models.
Localized analytics for data sovereignty and latency
The solution processes drivetrain data entirely within the user’s infrastructure, eliminating the need for cloud connectivity. This approach addresses requirements in industries where data sovereignty, cybersecurity, or isolated network architectures are critical. By running analytics on industrial PCs, the system ensures low-latency evaluation of operational data and supports real-time decision-making.
The software architecture is containerized, allowing modular deployment and scalability across different industrial environments. It supports standard industrial communication protocols, including MQTT, gRPC, and OPC UA, enabling integration into SCADA systems, edge platforms, and maintenance software.
High-resolution data acquisition and preprocessing
The system captures high-resolution data streams, including vibration signals, analog values, and diagnostic fingerprint information. These inputs are synchronized using Precision Time Protocol (PTP), ensuring accurate temporal alignment across multiple data sources.
Data acquisition is performed through dedicated connection modules, depending on the application requirements:
- Vibration monitoring modules for detailed mechanical analysis
- Fast process parameter modules for capturing dynamic system behavior
- IoT modules for integrating additional sensor and automation data
All data is preprocessed locally before being analyzed, reducing bandwidth requirements and ensuring data integrity within the system.
AI-based condition monitoring and anomaly detection
The integrated Industrial AI applies pattern recognition and anomaly detection techniques to identify deviations from typical drivetrain behavior. By continuously analyzing operational patterns, the system detects early-stage wear, mechanical changes, or process irregularities.
The user interface provides multiple levels of insight, including plant-wide overviews, key performance indicator (KPI) trends, and detailed diagnostic views. These are accessible via a standard web browser, supporting ease of use without requiring specialized client software.
Application across variable operating conditions
The solution is designed for industrial systems with variable loads, speeds, and operating profiles. Typical applications include production machinery such as extruders, packaging systems, and textile equipment, where early detection of mechanical deviations can prevent downtime.
It is also applicable in infrastructure systems such as pumps, compressors, and conveyor systems, which often operate continuously under changing conditions. In motion-control applications, the system enables detailed analysis of dynamic load changes and transient operating states.
Positioning within modular drivetrain analytics
The on-premises solution complements Siemens’ existing cloud-based drivetrain analytics offering, which supports cross-site analysis and fleet-level optimization. Both approaches follow a modular architecture but differ in deployment models and integration environments.
By enabling localized data processing combined with AI-driven diagnostics, the system supports predictive maintenance strategies and enhances operational reliability in industrial drive systems.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
AI-based condition monitoring and anomaly detection
The integrated Industrial AI applies pattern recognition and anomaly detection techniques to identify deviations from typical drivetrain behavior. By continuously analyzing operational patterns, the system detects early-stage wear, mechanical changes, or process irregularities.
The user interface provides multiple levels of insight, including plant-wide overviews, key performance indicator (KPI) trends, and detailed diagnostic views. These are accessible via a standard web browser, supporting ease of use without requiring specialized client software.
Application across variable operating conditions
The solution is designed for industrial systems with variable loads, speeds, and operating profiles. Typical applications include production machinery such as extruders, packaging systems, and textile equipment, where early detection of mechanical deviations can prevent downtime.
It is also applicable in infrastructure systems such as pumps, compressors, and conveyor systems, which often operate continuously under changing conditions. In motion-control applications, the system enables detailed analysis of dynamic load changes and transient operating states.
Positioning within modular drivetrain analytics
The on-premises solution complements Siemens’ existing cloud-based drivetrain analytics offering, which supports cross-site analysis and fleet-level optimization. Both approaches follow a modular architecture but differ in deployment models and integration environments.
By enabling localized data processing combined with AI-driven diagnostics, the system supports predictive maintenance strategies and enhances operational reliability in industrial drive systems.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
