Diagnosing simultaneous faults using the local regularity of vibration signals

Abstract

The regularity of the vibration signals measured from a rotating machine is often affected by the condition of the machine. The fractional order of regularity can be measured using the definition of Hölder continuity. In this paper, we review the connection between the pointwise Hölder regularity of a signal and its wavelet transform. We calculate the wavelet transform modulus of acceleration measurements from a test rig. The effects of different faults were recorded, such as unbalance, the coupling misalignment of a claw clutch, the absence of lubrication in a ball bearing, the absence of the bearing's cage, and their combinations. An analysis of the estimated isolated pointwise regularities from the wavelet transform modulus maxima ridges shows that the faults often cause irregularities in the signals and that their locations and frequencies can be used in diagnosing the faults. Coupling misalignment and the absence of lubrication in a ball bearing both cause impact-like vibrations, but these impacts have positive and negative regularities in the case of a coupling misalignment and mainly negative in the case of a dry bearing. Unbalance is best diagnosed from the integrals of the acceleration signals using traditional methods. In diagnosing the misalignment, bearing problems and simultaneous faults, the local regularity analysis outperforms the use of high order norms of differentiated acceleration measurements (i.e. jerk and snap signals). Using just three features (the number of local irregularities in an acceleration signal, their mean Hölder regularity and the arithmetic mean of the absolute values of a velocity signal), a quadratic classifier can be constructed whose estimated classification error is only 0.3 %.

Bearing Defect and Misalignment Diagnostics using Local Regularity and Sparse Frequency Analysis

“…Even in Cases 0 and 1 there are a couple of irregularities with exponents that are less than -3. Such large negative values were also observed in (Nissilä and Laurila, 2019;Kotila et al, 2010) in cases of dry bearing and local bearing faults. We also observe that the amount of irregularities increases with the load level and their constants A get bigger.…”

“…When these local regularity signals are calculated from vibration measurements of machines, they may contain useful diagnostic information. They have been shown to be useful for diagnosing for example gear tooth cracks and completely lost gear teeth (Loutridis and Trochidis, 2004), local bearing defects (Kotila et al, 2010) and mis-alignment of a claw clutch and bearing lubrication problems (Nissilä and Laurila, 2019). In (Miao and Makis, 2007) a feature vector calculated from the wavelet modulus maxima ridges is fed to a hidden Markov model for fault classification.…”

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…Pham_2020 [131] Ambika_2019 [132] Nissila_2019 [133] Tong_2018 [134] Jayakumar_2017 [135] Huo_2017 [136] Li_2016c [137] Hua_2015 [138] Gelman_2015 [139] Gelman_2014 [140] Tse_2013a [141] Li_2013a [142] Luo_2003 [143] He_2017 [144] Kawada_2003 [145] Gelman_2020 [146] Hartono_2019 [147] Puchalski_2019 [148] Gelman_2017a [149] Gelman_2017b [150] Stander_2002 [151] Shu_2020 [152] Liu_2019b [153] Jayakumar_2017 [135] Antoni_2002 [154] Xiao_2020 [11] Liu_2019a [155] You_2019 [156] Antoni_2006 [157] Signal decomposition (EMD, EEMD, LMD, SVD, VMD)…”

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach

“…Another notable advancement is feature fusion and selection, where machine learning algorithms like support vector machines (SVMs) and genetic algorithms (GAs) are employed to fuse relevant features and optimize feature selection [25][26][27][28][29][30][31]. This approach has demonstrated efficacy in improving fault diagnosis accuracy by focusing on the most informative aspects of the data.…”

“…Another significant advancement in the field is the utilization of feature fusion and selection techniques. Researchers have delved into the application of machine learning algorithms, including support vector machines (SVMs) and genetic algorithms (GAs), to effectively integrate relevant features and streamline the feature selection process [25][26][27][28][29][30][31][32].…”