Abstract
When a part of the loader’s gearbox fails, this can lead to equipment failure due to the complex internal structure and the interrelationship between the parts. Therefore, it is imperative to research an efficient strategy for transmission fault diagnosis. In this study, the non-contact characteristics of noise diagnosis using sound intensity probes were used to collect noise signals generated under gear breaking conditions. The independent component analysis (ICA) technique was applied for feature extraction from the original data and to reduce the correlation between the signals. The correlation coefficient between the independent components and the source data was used as the input parameters of the support vector machine (SVM) classifier. The separation of the independent components was achieved by MATLAB simulation. The misdiagnosis rate was 5% for 40 sets of test data. A 13-point test platform for noise testing of the loader gearbox was built according to Chinese national standards. Source signals under the normal and fault conditions were analyzed separately by ICA and SVM algorithms. In this case, the misdiagnosis rate was 7.5% for the 40 sets of experimental test data. This proved that the proposed method could effectively realize fault classification and recognition.
Highlights
As the most widely used type of engineering machinery, a loader is commonly utilized in construction sites such as roads, railways, and ports
The FastICA principle is used to separate the independent sources in the signal, and the correlation coefficients thenoise noise signal and theindependent independent source are used construct and the correlation coefficients ofof the signal and the source are used totoconstruct the eigenvector the noise signal
Thewear correlation coefficients between thethe independent thebysource signals in gear under the two conditions and masking of components the vibrationand effect random noise.from each working condition are obviously smaller than the results of the simulation because the signal has been separated into seven independent components
Summary
As the most widely used type of engineering machinery, a loader is commonly utilized in construction sites such as roads, railways, and ports. Principal component analysis (PCA) and ICA techniques were applied andof diagnosis, George, is and able the to accurately and isolate theofroot to thedetection diagnosis turbinesproposed for faultbydetection, validity detect and effectiveness thecauses approach for each individual fault [10]. The French scholar Badaoui used ICA in combination with a Wiener filter to to the diagnosis of turbines for fault detection, and the validity and effectiveness of the approach separate combustion noise and piston slap in a diesel engine [12]. Many improved algorithms such were verified [11].
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