Abstract:Introducing the complex wavelet transform into the fault feature extraction of automobile main reducer, a fault feature extraction method for automobile main reducer based on complex Morlet wavelet transform was proposed. Aimed at the characteristics of main reducer vibrating signal, two groups of compounding information for the extraction of mechanical fault feature were constructed according to the magnitudes and phases of complex wavelet transform. The simulation results show that the phases of complex wavelet transform and compounding information are more sensitive to singular points of signal than magnitudes, which can extract singularity of signal efficiently and position the singular points of signal accurately. Subsequently, the fault signal of automobile main reducer was analyzed by complex wavelet transform with its compounding information and real wavelet transform respectively. The results show that main reducer fault feature points can be positioned accurately by compounding information, and the decomposition just needs scale 1 calculation, which reduces the calculation greatly for fault feature extraction. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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