Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

Abstract

Multifractal detrended fluctuation analysis (MFDFA) is proved to be a powerful tool for fault diagnosis of rotating machinery due to its ability to reveal multifractal structures hidden in nonstationary and nonlinear vibration signals. To overcome the discontinuity of the fitting scale-dependent trend and the poor adaptability of this algorithm, Empirical Mode Decomposition-Multifractal Detrended Fluctuation Analysis (EMD-MFDFA) is introduced. However, EMD-MFDFA runs into difficulties in reverse segmentation and the selection of the expected Intrinsic Mode Functions (IMFs). Aiming at solving these deficiencies, a Modified EMD-MFDFA (MEMD-MFDFA) approach with IMF selection strategy and Step-Moving Window (SMW) segmentation method is proposed in this paper. In MEMD-MFDFA, a metric for distinguishing deterministic and random components is established to select expected IMF components by scaling exponent. Meanwhile, SMW segmentation method is exploited to reduce the estimated errors caused by reverse segmentation. The robustness of the proposed method is investigated through comparing MEMD-MFDFA, MFDFA, and EMD-MFDFA by multifractality of simulated signals with different Signal-to-Noise Ratio (SNR). Furthermore, the proposed approach is applied to three bearing run-to-failure datasets containing three types of faults, and the results show that the multifeatures of the multifractal spectrum obtained by MEMD-MFDFA have the ability to simultaneously identify early fault and assess performance degradation of bearings.

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

“…Bearing defects mainly include fatigue spalling, wear, corrosion and cracks, which usually cause repetitive shock vibrations and make the bearing vibration signal exhibit impulsive features and sparse structure 6 . Repetitive impulse features are often regarded as typical symptoms of bearing faults and have become an important basis for health monitoring and diagnostics [7][8][9] . The health monitoring indicators based on signal sparsity have been deeply developed, and the constructed impulse feature extraction method can effectively realize fault diagnosis [10][11][12] .…”

“…6 Repetitive impulse features are often regarded as typical symptoms of bearing faults and have become an important basis for health monitoring and diagnostics. 7–9 The health monitoring indicators based on signal sparsity have been deeply developed, and the constructed impulse feature extraction method can effectively realize fault diagnosis. 10–12 Sparsity measures, such as kurtosis, negentropy (NE), ratio of L2 norm to L1 norm (L2/L1), and Gini index (GI), are generally recognized sparse quantification tools 13 and have been widely employed in condition monitoring and fault diagnosis of rolling bearings.…”

Bearing fault diagnosis method based on comprehensive information divergence and improved BP-AdaBoost algorithm

“…As one of the decisive components of rotating machinery, rolling bearings are used in various engineering fields such as transportation, aviation, and precision machine tools. 3 The fault diagnosis of rolling bearings is an important guarantee for the long-term safety and stability of the mechanical system. In recent years, industrial technology has continued to mature, and mechanical equipment has developed toward high complexity and high speed 6 , which imposes more stringent requirements for the normal operation of mechanical equipment; so, it is very important to monitor the running state of rolling bearings.…”

Nonlinear Vibration Feature Recognition Method for Reciprocating Compressor Cylinder Based on VMD-Multifractal Spectrum

“…It could divide a complex fractal into many small regions with diferent degrees of singularity, which is suitable for the self-similarity of complex systems analysis. Multifractal spectrum can describe the singularity of non-linear vibration signals well [20][21][22].…”