IGIgram: An improved Gini index-based envelope analysis for rolling bearing fault diagnosis
The transient impulse features caused by rolling bearing faults are often present in the
resonance frequency band which is closely related to the dynamic characteristics of the …
resonance frequency band which is closely related to the dynamic characteristics of the …
Generalized Gini indices: Complementary sparsity measures to Box-Cox sparsity measures for machine condition monitoring
Sparsity measures that can quantify the sparsity of signals are often used as objective
functions of signal processing and machine learning algorithms (eg, sparse filtering …
functions of signal processing and machine learning algorithms (eg, sparse filtering …
Probability distributions and typical sparsity measures of Hilbert transform-based generalized envelopes and their application to machine condition monitoring
The establishment of probability distributions of machine vibration signals is crucial for
calculating theoretical baselines of machine health indicators. Health indicators based on …
calculating theoretical baselines of machine health indicators. Health indicators based on …
Continuous health monitoring of rolling element bearing based on nonlinear oscillatory sample entropy
As a nonlinear measure, sample entropy (SE) can be considered a suitable parameter for
characterizing rolling element bearing health status by measuring complexity of vibration …
characterizing rolling element bearing health status by measuring complexity of vibration …
Multi-node feature learning network based on maximum spectral harmonics-to-noise ratio deconvolution for machine condition monitoring
Q Zhou, C Yi, L Yan, C Huang, X Song… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Since the cyclostationarity in vibration signals is the key to judge the rotating machine health
state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single …
state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single …
Interpretable sparse learned weights and their entropy based quantification for online machine health monitoring
Incipient fault detection and diagnosis provide a firm grounding for machine health
monitoring. Nevertheless, fault signatures at the time of an incipient fault are extremely weak …
monitoring. Nevertheless, fault signatures at the time of an incipient fault are extremely weak …
A full generalization of the Gini index for bearing condition monitoring
The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as
sparsity measures for sparse quantification and machine condition monitoring. The …
sparsity measures for sparse quantification and machine condition monitoring. The …
Oscillatory Lempel–Ziv complexity calculation as a nonlinear measure for continuous monitoring of bearing health
As a nonlinear measure, Lempel–Ziv complexity (LZC) can be considered as a suitable
parameter for characterizing bearing health status by measuring the complexity of vibration …
parameter for characterizing bearing health status by measuring the complexity of vibration …
Generalized statistical indicators-guided signal blind deconvolution for fault diagnosis of railway vehicle axle-box bearings
Vibration impulses caused by surface defects of railway vehicle axle-box bearings are
important features for fault diagnosis. To accurately diagnose axle-box bearing surface …
important features for fault diagnosis. To accurately diagnose axle-box bearing surface …
Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring
The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields
of machine condition monitoring and fault diagnosis, and they can be reformulated as the …
of machine condition monitoring and fault diagnosis, and they can be reformulated as the …