Vibration Signal Classification for Rotating Device Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vibration analysis methods for monitoring rotating devices in infrastructure systems, such as oil production and water supply, struggle to adapt to changes in mechanical conditions, leading to delayed fault detection and increased maintenance costs due to sudden or gradual degradation of device characteristics.
Innovation Solution
An apparatus and method that utilize a control module to process vibration signals from sensors, separating base and side frequencies through pre-processing and applying one-class classification algorithms like support vector machines, generating a confidence level to indicate error status and predict faults, thereby enabling early maintenance planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vibration analysis methods are used for monitoring rotating devices, then the monitoring system is simple to implement, but the system cannot adapt to changes in mechanical conditions leading to delayed fault detection
Solution Approach 1:
The patent segments the vibration spectrum into multiple frequency bands (e.g., low frequency band 0-10 Hz, medium frequency band 10-100 Hz, high frequency band 100-1000 Hz) and applies different analysis methods to each band. This allows the system to adapt to different mechanical conditions in each frequency range while maintaining overall system reliability without excessive complexity.
Solution Approach 2:
The monitoring system dynamically adjusts its analysis parameters and thresholds based on detected changes in mechanical conditions. When deviations from baseline vibration patterns are detected, the system adapts its frequency band configurations and classification thresholds, enabling reliable fault detection across varying operational conditions.
2Adaptability or versatility
If mechanical conditions of the device change due to amendments, then the device adapts to new operational requirements, but the vibration signature changes making further monitoring difficult
Solution Approach 1:
The system performs preliminary baseline vibration analysis under known good mechanical conditions to establish reference frequency bands and vibration signatures. When mechanical amendments occur, the system compares current vibration patterns against these pre-established baselines, enabling continuous monitoring precision despite changes in device adaptability.
Solution Approach 2:
The monitoring system continuously feedbacks vibration analysis results and automatically adjusts frequency band configurations and analysis parameters based on detected mechanical condition changes. This closed-loop feedback mechanism maintains measurement precision even as the device adapts to new operational requirements through mechanical amendments.
3Reliability
If comprehensive vibration analysis is performed to improve error detection, then fault detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on specific frequency bands most relevant to potential faults rather than analyzing the entire spectrum uniformly. This selective approach maintains high error detection accuracy for critical frequency ranges while reducing overall processing time and computational burden.
Solution Approach 2:
The comprehensive vibration analysis is performed periodically at optimized intervals rather than continuously, with the frequency of analysis adjusted based on device operational status and risk levels. This periodic action maintains reliable error detection capability while significantly reducing processing time and resource consumption compared to continuous analysis.
Data Source
AI summary
An apparatus for monitoring of a device including a moveable part, especially a rotating device, wherein the apparatus includes a control module which receives a measured vibration signal of the device provided by a sensor connected to the device, provides a spectrum of the measured vibration signal, pre-processes the spectrum to determine base frequencies and side frequencies, where the base frequencies are frequencies having peak powers corresponding to eigen frequencies of the device or faulty frequencies and the side frequencies correspond to other frequencies, where the control module additionally processes the base and side frequencies by applying separately a one-class classification on the base and side frequencies, combines the results of the one-class classifications to obtain a classification signal representing a confidence level, and outputs a decision support signal based on the classification signal, where the decision support signal indicates an error status of the monitored device.