Slide Bearing Rubbing Detection via Cepstrum Analysis
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Solution Overview
Problem
Conventional methods using shaft vibration sensors or piezoelectric acceleration sensors struggle to accurately detect minor rubbing abnormalities in slide bearings, especially in industrial settings like turbines and diesel engines, due to interference from vibration noise associated with piston movement and burst or supply/exhaust noises, leading to poor diagnostic accuracy.
Innovation Solution
The method involves transforming acceleration waveform data into a frequency domain using Fourier transform, quantifying peak information at rotational frequency intervals, and applying cepstrum, auto-correlation, or cross-correlation analysis to detect frequency modulation, allowing for early and accurate detection of minor rubbing abnormalities by monitoring characteristic values against predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vibration detection methods using acceleration sensors are used, then the detection system is simple and easy to operate, but the detection precision for minor rubbing abnormalities is insufficient due to interference from piston movement vibration and burst noise
Solution Approach 1:
The patent transforms the vibration signal from time domain to frequency domain using Fourier transform, then applies cepstrum analysis to extract characteristic frequency parameters. This parameter transformation enables precise detection of rubbing abnormalities by identifying specific frequency modulation patterns that distinguish rubbing from other vibration sources like piston movement and burst noise
Solution Approach 2:
The patent replaces conventional mechanical vibration analysis methods with signal processing techniques including Fourier transform and cepstrum analysis. This substitution allows for automated, high-precision detection of rubbing abnormalities by analyzing frequency domain characteristics rather than relying on simple time-domain vibration amplitude measurements
2Measurement precision
If envelope detection process is applied to acoustic signals, then the detection method can identify rubbing, but the diagnostic accuracy is poor because it focuses only on amplitude modulation component and ignores frequency modulation
Solution Approach 1:
The patent transitions from analyzing only amplitude modulation in the time domain to analyzing frequency modulation in the frequency domain through Fourier transform. By examining the frequency spectrum and applying cepstrum analysis, the method captures frequency modulation characteristics that were previously ignored, thereby improving diagnostic accuracy without losing critical information
Solution Approach 2:
The patent introduces cepstrum analysis as an intermediary processing step between Fourier transform and final diagnosis. This intermediary technique effectively separates frequency modulation components from amplitude modulation components, allowing for accurate identification of rubbing characteristics while filtering out irrelevant information from other vibration sources
3Reliability
If early detection of minor rubbing is achieved through frequency modulation analysis, then equipment damage can be prevented, but the detection method becomes more complex requiring Fourier transform and cepstrum analysis
Solution Approach 1:
The patent applies preliminary signal processing steps (Fourier transform followed by cepstrum analysis) to transform the raw vibration signal into a form where rubbing characteristics are clearly visible. This preliminary action prepares the data in advance, enabling reliable early detection of minor rubbing before it progresses to severe damage, while the automated nature of the processing keeps the implementation manageable
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
This approach significantly enhances the sensitivity of abnormality detection, differentiating between rubbing and backlash, and enables early identification of minor rubbing phenomena, preventing equipment damage and extending maintenance intervals.
Implementation Method 1
a piezoelectric acceleration sensor (12)
Implementation Method 2
transforming acceleration waveform data into an acceleration spectrum of a frequency domain by applying a Fourier transform to the acceleration waveform data
Implementation Method 3
perform a cepstrum calculation which applies an inverse Fourier transform after a logarithmic transformation is applied to an acceleration spectrum
Data Source
AI summary
A sign of a minor rubbing abnormality of a slide bearing in a diesel engine is accurately detected. In order to realize the accurate detection, the following is performed: detecting waveform data which represents an acceleration of a vibration which occurs when a slide bearing is in operation; transforming acceleration waveform data into an acceleration spectrum of a frequency domain by applying a Fourier transform to the acceleration waveform data; quantifying a plurality of peak information which occurs at a rotational frequency interval of a shaft to be measured in the acceleration spectrum by performing a predetermined signal process combined with rotational frequency information of the shaft; obtaining a characteristic value; monitoring whether the obtained characteristic value has exceeded a predetermined threshold value; and when the characteristic value has exceeded the threshold value, determining that an abnormality has occurred in the slide bearing.