Rotating Machinery Failure Detection Using Vibration Frequency Models
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Solution Overview
Problem
Conventional methods for detecting failure modes in rotating machinery require experienced engineers and are limited by analyzing narrow frequency windows, leading to reduced accuracy and inefficiency in predicting machine failures.
Innovation Solution
A system that uses vibration data to generate a frequency spectrum, comparing it to predetermined frequency models associated with failure modes, enabling automated and accurate detection of failure modes by determining similarity metrics across the entire frequency range, thereby improving maintenance scheduling and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use narrow frequency window analysis, then the analysis process is simple, but the detection accuracy is limited
Solution Approach 1:
The patent segments the frequency spectrum analysis into multiple narrow frequency windows, each analyzed independently. This allows the system to maintain the simplicity of narrow window analysis while covering the entire frequency range through multiple segments, thereby improving detection accuracy without requiring complex full-spectrum analysis methods
Solution Approach 2:
The patent applies partial action by analyzing only specific frequency windows that are most relevant to detecting particular failure modes. Instead of analyzing the entire frequency spectrum with equal detail, the system focuses computational resources on critical frequency ranges, improving accuracy for specific failure detections while maintaining overall system simplicity
2Productivity
If automated detection is implemented, then efficiency and productivity improve, but the system complexity increases
Solution Approach 1:
The patent uses copying by creating a digital model (frequency spectrum) of the vibration data and comparing it against predetermined failure mode signatures. This allows automated detection without requiring physical inspection or complex mechanical analysis systems, improving efficiency while keeping the system relatively simple through software-based pattern recognition
Solution Approach 2:
The patent replaces manual engineer analysis (mechanical/expert system) with automated computational algorithms. The system substitutes human expertise with computer-based frequency analysis and pattern matching, dramatically improving detection efficiency and productivity while reducing the need for highly specialized human resources
3Measurement precision
If full frequency spectrum is analyzed, then detection accuracy improves, but the analysis time increases
Solution Approach 1:
The patent segments the full frequency spectrum into multiple narrow frequency windows that can be analyzed independently and in parallel. This segmentation allows the system to achieve comprehensive frequency coverage for accurate failure mode detection while reducing the computational burden and analysis time by dividing the problem into smaller, manageable segments
Solution Approach 2:
The patent implements periodic action by continuously monitoring vibration data and periodically updating the frequency spectrum analysis. This allows the system to detect failure modes in real-time or near-real-time by analyzing data in periodic intervals rather than requiring long continuous analysis periods, improving both accuracy and time efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides improved accuracy in predicting failure timelines and types, enabling targeted maintenance and reducing unexpected downtime through real-time, automated failure mode detection.
Implementation Method 1
a sensor monitoring a machine to generate a frequency spectrum
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated failure mode detection for rotating machinery based on vibration analysis. To illustrate, a computing device may receive vibration data from one or more sensors configured to measure vibrations of a rotating machine, such as an engine. The computing device may generate a frequency spectrum based on the vibration data (or receive the frequency spectrum) and compare the frequency spectrum to one or more predetermined frequency models to determine one or more similarity metrics. The one or more predetermined frequency models may each be associated with a respective failure mode of the rotating machine. The computing device may identify a failure mode associated with a predetermined frequency model that corresponds to a similarity metric that satisfies a threshold, and the computing device may output an indication of the identified failure mode.