A New Incipient Fault Diagnosis Method Combining Improved RLS and LMD Algorithm for Rolling Bearings With Strong Background Noise

• Published in: IEEE access Vol. 6; pp. 26001 - 26010
• Main Authors: Darong, Huang, Lanyan, Ke, Bo, Mi, Ling, Zhao, Guoxi, Sun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Background noise; Bearing mechanical signal; Bearing strength; Bearings; Convergence; Decomposition; Demodulation; Equalization; Fault detection; Fault diagnosis; Feature extraction; Heuristic algorithms; improved RLS; incipient fault diagnosis; local decomposition algorithm; Noise; noise elimination; Noise measurement; Noise reduction; Roller bearings; Signal processing; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2829803

Aiming at the difficulty of extracting information for incipient fault symptoms from rolling bearings with strong background noise, an improved incipient fault detection method based on modified recursive least squares (RLS) adaptive equalization, and a local mean decomposition (LMD) algorithm is proposed. First, an efficient RLS de-noising model is established by introducing a momentum factor together with a forgotten factor to de-noise the incipient fault signal of the bearings. Then, the LMD algorithm is used to decompose the pre-processed signal to obtain an effective PF component, and complete the envelope demodulation to extract information from the incipient fault. Based on the above algorithm, an improved RLS and LMD identifying algorithm for incipient faults can thus be achieved. Finally, some actual fault signals of a large unit rolling bearing are used to simulate and verify the accuracy and efficiency of the proposed algorithm. The experimental comparison indicated that our algorithm can not only improve the de-noising effect, but also correctly extract the features of the incipient fault and identify them with good engineering operability and expansibility.