Advanced vibration-analysis solution empowers predictive maintenance and health management.
- ISO 10816-3 compliant and advanced frequency analysis
- Real-time AI analysis and on-site motor modeling
- EdgeX development framework accelerates secondary data utilization
- Intuitive notifications for motor-vibration anomalies
- Continuous software enhancements and updates
- Flexible architecture adjustment for on-premise and cloud
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Predicting health, preventing problems
ASUS IoT AISPHM is a revolutionary solution for predictive maintenance and health management. Leveraging real-time AI and state-of-the-art vibration analysis, AISPHM is specially designed to detect early-stage issues in rotating equipment, ensuring unparalleled reliability and performance in the most demanding factory environments — delivering immediate, intuitive alerts and the adaptability of a flexible architecture for optimized operational efficacy, either on-premise or in the cloud.
This innovative approach empowers industries to anticipate and swiftly resolve issues, reducing downtime and maintaining uninterrupted production flow. AISPHM stands out by preserving the health and longevity of industrial equipment, delivering continuous advancements, and providing ultimate peace of mind.
Low to no code for exceptional simplicity
Your Path to Operational Excellence
Combining ISO-10816-3 with FFT Spectrum AI Modeling
Dedicated AI models are established for each device, continuously monitoring their operational status. Any deviations from the original AI model are recorded as abnormal events.
Web-based Private and Public Cloud Architecture
A fully containerized architecture enhances deployment flexibility across multiple platforms, including PCs, smartphones, and tablets.
Cost-effective CPU-based Modeling and Inference
Intel i5/i7 machines can support data processing for up to 120 sensors without the need for additional GPU resources.
Supports the EdgeX Open Source Framework
For diverse industrial applications, modules can be developed for data reuse without the need for extensive code refactoring.
Data visualization meets predictive intelligence
Based on the production line, machine train, machine and the sensor measurement point, managers can quickly grasp vibration history data and sensor connectivity status, event notifications — as well as perform advanced inspections or lubrication tests based on anomaly types.
AISPHM combines the features of FFT spectrum modeling and ISO 10816-3 for comprehensive mechanical vibration monitoring. ISO 10816-3 offers overall vibration assessment, while FFT provides frequency domain analysis, early fault detection, multi-channel analysis, and quantitative evaluation. This integration allows for more in-depth and comprehensive fault detection and equipment health monitoring.
The same motor, installed on different ground and under varying loads, requires tailored models. You can configure different data collection intervals, automatically compare anomaly data once modeling is complete and provide detailed spectra for data labeling, maintaining model relevance.
Versatile framework for wired and wireless sensors
ASUS IoT provides the necessary hardware, software and algorithms for a versatile framework that supports both wired and wireless sensors. In particular, ASUS IoT PE100A offers connectivity for 12 wired sensors, while the PHM can simultaneously handle data modeling and anomaly detection for hundreds of sensors.
Make Prognostic and Health Management Simple
Creating vibration-data records for key machinery
By analyzing vibration data, AISPHM allows managers to conduct trend analyses on vital machinery. This proactive approach aids in early issue detection, ensuring timely interventions and machinery longevity.
Evaluate the effectiveness of repairs on rotating equipment
AISPHM excels in comparing data before and after motor repairs, pinpointing discrepancies, enabling users to gauge repair effectiveness and uncover potential underlying maintenance issues.
AI-enhanced non-destructive monitoring for abnormal resonance and temperature
AISPHM leverages AI to analyze data for non-destructive anomaly detection, specifically identifying unusual resonance and temperature patterns, ensuring equipment integrity without invasive checks.