Technical Overview ：

The VMS leverages multiple machine learning and deep learning algorithms—including Isolation Forest, Neural Networks, and others—to perform anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) prediction. Trained on a proprietary dataset exceeding 100,000 fault samples (primarily from small to medium motors), the AI models achieve anomaly detection accuracy above 90%. The system automatically learns the normal vibration signature (baseline behavior) of each asset and issues real-time alerts upon deviation.

Key Capabilities ：

• Anomaly Detection: Employs Isolation Forest algorithm to build a normal operation model from historical data, enabling rapid identification of deviant vibration patterns.

• Fault Diagnosis: Utilizes Neural Networks to analyze vibration signatures and classify root causes, such as shaft imbalance, bearing wear/damage, gear tooth wear, misalignment, etc.

• Remaining Useful Life (RUL) Prediction: Applies regression models (e.g., linear regression) or advanced time-series models like LSTM (Long Short-Term Memory) networks to forecast remaining operational life based on vibration trend progression.

Technical Overview ：

VMS employs spectrum-based frequency domain analysis using Fast Fourier Transform (FFT) to convert time-domain vibration signals into the frequency domain. This decomposes the signal into distinct frequency components, allowing precise diagnosis of the health condition of individual machine elements (bearings, impellers, gears, etc.). Each component generates characteristic fault frequencies during operation; the system generates spectrum plots and pinpoints anomaly sources accordingly.

Technical Advantages ：

Compared to conventional overall RMS (Root Mean Square) vibration monitoring, frequency band separation delivers significantly higher diagnostic resolution and fault localization capability. For example:

Technical Overview：

VMS supports dual transmission architectures—wireless (Wi-Fi / 5G) and wired (Ethernet)—with configurable sensor types and system topology tailored to specific application scenarios. Wireless solutions excel in hard-to-wire environments (e.g., mobile equipment, remote sites), while wired configurations ensure maximum data reliability in high-stability settings (e.g., semiconductor cleanrooms).

Technical Advantages：

• High Flexibility: Customers can mix wireless and wired deployments based on site requirements.

• Sensor Compatibility: Supports various vibration transducers (accelerometers, velocity sensors, etc.) with customizable monitoring parameters per asset type.

Generated Metrics：

Leveraging AI and frequency band analysis, VMS produces the following high-value indicators and health metrics:

◆ Health Score: A composite 0–100 score derived from vibration severity (referenced against ISO 10816 standards), stability trends, and RUL estimation.

◆ Remaining Useful Life (RUL): Projected in operating hours or cycles (e.g., “Bearing RUL: approximately 5,000 hours”).

◆ Automated Fault Diagnosis Report: Includes anomaly frequency distribution plots and root cause analysis with actionable recommendations (e.g., “Inner race bearing wear detected – recommend replacement during next scheduled shutdown”).

◆ Data-Driven Applications: These metrics and reports serve as direct inputs for maintenance planning, enabling optimized PM schedules, minimized unplanned downtime, and extended asset lifespan.

◆ Competitive Differentiation: Most vibration monitoring solutions available in Taiwan provide only basic vibration intensity data (RMS levels) without integrated health scoring or predictive analytics. Yuchens VMS delivers superior practical value through its end-to-end predictive intelligence.