Vibration Error Screening for Reliable Structural Monitoring Data

The patent applies preliminary action by establishing error detection rules and machine learning models before actual vibration measurement and analysis. The system pre-processes vibration data to identify potential measurement errors using predetermined criteria, such as checking for abnormal amplitude values, frequency patterns, or signal characteristics that indicate measurement failures. This preliminary error detection prevents contaminated data from entering the main analysis pipeline, thereby maintaining both measurement reliability and data accuracy throughout the monitoring process.

The patent introduces an intermediary error detection and filtering layer between raw vibration data collection and machine learning analysis. This intermediary system uses automated rules and algorithms to identify, flag, or remove measurement error data before it reaches the specialized machine learning models. By acting as a mediator, this layer protects the automated analysis process from being compromised by poor-quality data, thus maintaining both high automation extent and diagnosis reliability simultaneously.

The patent implements feedback mechanisms that continuously monitor the quality of collected vibration data throughout the extended measurement period. The system uses real-time error detection algorithms to identify degradation in data quality and provides feedback to adjust measurement parameters, trigger re-calibration, or exclude problematic data segments. This feedback loop enables the system to maintain high data quality standards even during continuous long-term monitoring, preventing the accumulation of measurement errors that would otherwise degrade overall data quality.