Periodically correlated random processes: Application in early diagnostics of mechanical systems

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

“…Javorskyj et al [145] proposed the covariance and spectral characteristics of periodically correlated random processes to describe the state of rotary mechanical systems. Igba et al [146] used RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes.…”

“…[127] Fast computation of the kurtogram Li et al [132] Particle Filter + Kurtogram Wang et al [125] Minimum entropy de-convolution + Fast Kurtogram Cong et al [129] Spectral kurtosis + autoregressive model Jeong et al [130] Spectral kurtosis Chen et al [133] Mean envelope Kurtosis + envelope analysis Jia et al [131] Maximum correlated kurtosis deconvolution Masmoudi et al [134] Time synchronous averaging Dong et al [135] Frequency-shifted bispectrum Zhou et al [136] Cyclic bispectrum Dong et al [137] Wigner-Ville spectrum Yuan et al [138] Multi-fractal analysis Siegel et al [139] Tachometer-less synchronously averaged envelope Park et al [140] Minimum variance cepstrum Fu et al [141] Adaptive fuzzy-means clustering Li et al [142] Informative frequency band Liu et al [143] Adaptive SR + quantum particle swarm Liao et al [144] Improved genetic algorithm Kedadouche et al [124] Approximate entropy + sample entropy + Lempel-Ziv Complexity. Javorskyj et al [145] Periodically correlated random processes Igba et al [146] Root mean square (RMS) + peak values Shao et al [147] RMS in angle domain Sharma et al [148] Modified time synchronous averaging Jin et al [149] Mahalanobis distance…”

Diagnostics of Rolling Bearings for Auxiliary Electromotors of Electric Locomotive Using Parametric Model and Envelope Spectrum

“…Нині методи вібродіагностування елементів роторного обладнання зазнають бурхливого розвитку у світі і засновані на поглибленому вивченні періодичних і майже періодичних нестаціонарних випадкових процесів і відповідних моделей [1]. Здійснюється удосконалення й доповнення стандартних спектральних, спектральночасових методів обробки сигналів, які базуються на класичному алгоритмі швидкого перетворення Фур'є (ШПФ) [2 -5].…”

An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

“…Reliable condition monitoring techniques are essential when performing condition-based maintenance on expensive rotating machine assets [1,2]. Advanced signal processing [3][4][5][6][7][8][9][10][11][12][13] and sophisticated supervised machine learning techniques [14][15][16][17][18][19][20][21][22][23] are actively investigated to improve the condition monitoring task. Deep learning techniques have also recently been used to not only infer the condition of the machine, but also to extract features from the raw dataset i.e.…”