Fault Frequency Set Detection for Multi-Vendor Machinery Components
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Solution Overview
Problem
In complex machinery, such as wind turbines, identifying failing bearings from different vendors is challenging due to varying fault frequencies, requiring cumbersome record-keeping and potential for errors, especially when multiple vendors' components are present.
Innovation Solution
A system that collects data from sensors, calculates spectra, and compares vendor-specific fault frequency ranges to identify fault frequencies, determining the likely vendor and location of failing components without needing detailed records for each bearing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed records are maintained for each bearing including fault frequency and location, then fault identification accuracy is improved, but system complexity and potential for error increase
Solution Approach 1:
The system enables self-identification of bearing faults by automatically comparing sensor data against stored vendor fault frequency ranges. The machine itself performs the identification function without requiring external record-keeping or manual tracking of bearing specifications.
Solution Approach 2:
Instead of maintaining physical records for each bearing, the system creates and maintains a digital copy in the form of stored vendor fault frequency ranges. These ranges serve as reference data that can be automatically compared against sensor readings to identify faults without requiring individual bearing records.
2Reliability
If sensors continuously monitor vibration data, then fault detection capability is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary action by pre-storing vendor fault frequency ranges before monitoring begins. This preparation allows the system to quickly compare sensor data against known ranges without requiring complex real-time analysis algorithms, thereby simplifying the data processing while maintaining reliable fault detection.
3Adaptability or versatility
If multiple vendor bearings are used in a machine, then component availability and selection flexibility are improved, but fault frequency identification difficulty increases
Solution Approach 1:
The system segments the fault frequency identification problem by creating separate vendor-specific fault frequency ranges for each bearing vendor. This segmentation allows the system to handle multiple vendors independently, comparing sensor data against the appropriate vendor's ranges to identify faults even when multiple different bearing types are present in the same machine.
Data Source
AI summary
Systems and methods are provided for monitoring operating machinery to identify fault frequency sets for consumable components used in the machinery. Data associated with characteristic behavior of the machinery being monitored is acquired and analyzed, comparing known vendor information regarding fault frequencies for specified components to vibration frequencies observed in the machine in service. The systems and methods described herein enable an operator to identify and confirm critical information such as the location of and/or vendor identity for components exhibiting fault vibration behavior.