The Average Coding Length of Huffman Coding Based Signal Processing and Its Application in Fault Severity Recognition

Abstract

The transient impact components in vibration signal, which are the major information for bearing fault severity recognition, are often interfered with by ambient noise. Meanwhile, for bearing fault severity recognition, the frequency band selection methods which are employed to pre-process the contaminated vibration signal only select the partial frequency band of the vibration signal and cause information loss of other frequency band. Aiming at this issue, this paper proposes a novel fault severity recognition method based on Huffman coding, which can retain all the information of the frequency band, and is applied for the first time to bearing fault severity recognition. Specifically, the average coding length of Huffman coding (ACLHC) of the original vibration signal is first calculated to reduce the noise and highlight the impact components of the signal. Then, the ACLHC is encoded by symbolic aggregate approximation (SAX) to reflect the modulation information of bearing. Finally, the Lempel‑Ziv indicator (LZ indicator) of the symbol sequence is calculated to reflect the fault severity. The proposed method is verified by the bearing datasets under different working conditions. Compared with the methods based on frequency band selection, the proposed method effectively recognizes the fault severity of bearing for more working conditions.

Keywords

• signal processing
• lempel‑ziv indicator
• average coding length of huffman coding
• symbolic aggregate approximation
• bearing fault severity