Spectral Plot Differentiation for Machine Fault Frequency Families
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Accurately differentiating between various spectral frequencies in machine vibration analysis is challenging due to numerous spectral peaks and harmonic families, making it difficult for analysts to identify fault frequencies and spectral band parameters in graphical displays.
Innovation Solution
Implementing methods such as color coding, different line types, and filtering to visually distinguish fault frequency peaks and harmonic families in spectral plots, allowing for clearer differentiation and visualization of machine condition data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all spectral peaks are displayed in a graphical plot, then complete spectral information is provided, but it becomes difficult to differentiate and identify specific fault frequency families
Solution Approach 1:
The patent segments the spectral data by separating fault frequency peaks from non-fault peaks and organizing them into distinct harmonic families. Each family is individually identified and can be selectively displayed, allowing analysts to focus on specific frequency groups without being overwhelmed by the complete spectrum.
Solution Approach 2:
The patent applies color coding to differentiate various harmonic families in the graphical display. Each fault frequency family is assigned a distinct color or line style, enabling visual differentiation and easy identification of specific frequency groups while maintaining complete spectral information in the background.
2Adaptability or versatility
If multiple harmonic families are displayed together, then comprehensive fault coverage is achieved, but individual family differentiation becomes difficult
Solution Approach 1:
The patent implements dynamic display capabilities that allow analysts to interactively select and emphasize specific harmonic families. The system can dynamically adjust the display to highlight selected families while maintaining context of other frequencies, enabling flexible analysis of different fault conditions without losing comprehensive coverage.
Solution Approach 2:
By assigning distinct colors and line styles to different harmonic families, the patent enables simultaneous display of multiple families with clear visual differentiation. This allows comprehensive fault coverage while maintaining ease of identification through color-coded family separation.
3Measurement precision
If spectral analysis requires detailed examination of all peaks, then accurate fault identification is possible, but analysis time increases significantly
Solution Approach 1:
The patent performs preliminary automated identification and organization of spectral peaks into harmonic families before the analyst begins examination. Fault frequency peaks are pre-sorted and tagged, allowing analysts to quickly navigate to relevant frequency groups without manually examining every peak, thus maintaining accuracy while reducing analysis time.
Solution Approach 2:
By pre-segmenting the spectrum into identified harmonic families with clear visual markers, the patent enables analysts to focus examination on specific frequency groups rather than scanning the entire spectrum. This segmented approach maintains identification accuracy while significantly reducing the time required to locate and analyze fault frequencies.
Data Source
AI summary
Spectral machine condition energy peaks are graphically represented in a spectral plot using color coding, different line types, and/or filtering. This allows visual differentiation of spectral peaks associated with various fault frequency families from one another, whereby a machine condition analyst using computer-based analysis software can easily see each family of spectral peaks individually, without all the other spectral peaks, or in combinations of families that are relevant to a machine fault under investigation. In addition to current spectral data, the analyst can also view a historical trend of related scalar parameters plotted in conjunction with current spectral data, wherein the spectral data plot is synchronized with a time-based cursor on the trend plot.