Abstract:
The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key
mechanical part. Any faults in the gearbox will greatly affect the quality o f the short filament fibres. However, due to
the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity o f the manufacturing line,
effective and efficient feature extraction o f gear faults is still a challenge. To this end, a new fault diagnosis method based
on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method,
wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is
obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence
matrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence
matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental
data acquired from a real-world manufacturing line o f short filament fibres are used to evaluate the performance o f the
proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is
capable o f accurate gear fault detection in fibre manufacturing lines.
