A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks

Z Chen, A Mauricio, W Li, K Gryllias - Mechanical Systems and Signal …, 2020 - Elsevier
Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating
machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have …

Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump

A Kumar, H Tang, G Vashishtha, J **ang - Mechanical Systems and Signal …, 2022 - Elsevier
The natural intermittent impulses created by impeller in case of centrifugal pump, and
reciprocating motions of piston in case of axial pump exhibit strong cyclo-stationary …

Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis

J Guo, Q He, D Zhen, F Gu, AD Ball - Reliability Engineering & System …, 2023 - Elsevier
When an abnormal situation occurs in rotating machinery, fault feature information may be
scattered on multiple sensors, and fault feature extraction through a single sensor is not …

Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating …

A Mauricio, WA Smith, RB Randall, J Antoni… - … Systems and Signal …, 2020 - Elsevier
Demodulation methods are widely used for bearing diagnostics, based often on the
(Squared) Envelope Spectrum after band pass filtering. One of the main challenges of these …

A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis

D Wang, X Zhao, LL Kou, Y Qin, Y Zhao… - Mechanical Systems and …, 2019 - Elsevier
Rolling element bearings are widely used in machines to support rotating shafts and their
health conditions degrade over time due to harsh working conditions. Once a fault occurs on …

Cyclostationary-based multiband envelope spectra extraction for bearing diagnostics: The combined improved envelope spectrum

A Mauricio, K Gryllias - Mechanical Systems and Signal Processing, 2021 - Elsevier
Bearing diagnostics is a field of intensive research, focusing nowadays mainly in
complicated machinery (eg planetary gearboxes, multi-stage gearboxes, etc.) operating …

Multi-component fault diagnosis of wheelset-bearing using shift-invariant impulsive dictionary matching pursuit and sparrow search algorithm

Z **ng, C Yi, JH Lin, QY Zhou - Measurement, 2021 - Elsevier
Wheelset-bearing is an important part of the bogie for high-speed train. Fault diagnosis of
wheelset-bearing is of great significance for the safety of the railway service. In the diagnosis …

Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study

B Chen, Y Cheng, W Zhang, F Gu, G Mei - ISA transactions, 2022 - Elsevier
Identifying a spectral frequency band with abundant fault information from spectral
coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind …

An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions

S Schmidt, PS Heyns, KC Gryllias - Mechanical Systems and Signal …, 2021 - Elsevier
The application of informative frequency band identification methods makes it possible to
enhance weak damage components in the vibration signals acquired from rotating …

Optimized weights spectrum autocorrelation: A new and promising method for fault characteristic frequency identification for rotating machine fault diagnosis

B Hou, X Feng, JZ Kong, Z Peng, KL Tsui… - Mechanical Systems and …, 2023 - Elsevier
Since fault characteristic frequencies (FCFs) and their harmonics are closely connected with
specific fault types of rotating machines, identification of FCFs and their harmonics is a very …