Cyclic Machine Condition Monitoring for Variable-Speed Fault Detection

The vibration signal analysis is segmented into discrete cycles of the machine component. Each cycle is divided into angular segments corresponding to specific positions of the component (e.g., bearing races). This segmentation allows for position-resolved vibration analysis without requiring constant rotation speed assumptions, as each angular position is analyzed independently within its cycle context.

The method transitions from static frequency domain analysis (FFT) to dynamic time-domain analysis where vibration signals are evaluated in the context of actual component motion cycles. The analysis adapts to variable speeds by synchronizing measurements with component position rather than time, allowing accurate breakdown detection even when rotation speed varies during operation.

The system performs automated self-diagnosis by processing its own vibration measurement data through cycle-resolved analysis algorithms. The method automatically identifies characteristic vibration patterns associated with different fault types (inner race, outer race, ball defects) and estimates remaining lifetime without requiring external expert intervention, thereby improving reliability while maintaining manageable complexity through algorithmic automation.

The system continuously monitors vibration signals and provides feedback on component health status. By comparing measured vibration characteristics against known fault signatures and tracking changes over multiple cycles, the system dynamically adjusts its assessment of component condition and remaining lifetime, improving reliability through continuous self-evaluation rather than static empirical methods.

The analysis method is fundamentally dynamic, synchronizing vibration signal acquisition and analysis with the actual motion cycles of the machine component rather than assuming constant speed. By using cycle-resolved analysis where each vibration cycle is evaluated in the context of the component's actual position and motion state, the system naturally adapts to variable speed profiles without requiring separate speed compensation mechanisms.

The method transitions from time-domain analysis (which requires constant speed assumptions) to angular-domain or cycle-domain analysis. By evaluating vibrations as a function of component angular position rather than time, the system adds a spatial dimension to the analysis that is independent of speed variations, allowing accurate fault detection whether the component rotates at constant or variable speed.

The vibration signal is segmented into discrete cycles and further divided into angular segments corresponding to specific positions of the machine component. This segmentation concentrates fault-related vibrations at their characteristic angular positions rather than spreading them across the entire frequency spectrum, improving detectability even when overall signal-to-noise ratio is low. The segmented approach allows identification of localized defects that would be smeared in traditional FFT analysis.

The system performs preliminary signal conditioning and cycle-synchronization before fault detection analysis. By pre-processing the vibration signals to align with component cycles and filter out unrelated vibrations, the method enhances the signal-to-noise ratio for fault-related frequencies before the actual detection step, improving precision without requiring excessively complex post-processing.