A Machine Learning Approach for Gearbox System Fault Diagnosis

Vrba, Jan; Cejnek, Matouš; Steinbach, Jakub; Krbcová, Zuzana

doi:10.3390/e23091130

Cited by 12 publications

(4 citation statements)

References 57 publications

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“…Literature on gearbox elements failures and their diagnosis are also available, for example in Refs. 59 – 61 . However, it is no less concerned with gears mounted on axles or shafts.…”

Section: Introductionmentioning

confidence: 99%

Influence of geometrical and operational parameters on tooth wear in the working mechanism of a satellite motor

Sliwinski

2023

Sci Rep

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This article describes the phenomena affecting the wear of the rotor of the working mechanism in a hydraulic satellite motor. The basic geometrical relationships that allow the calculation of the coordinates of the points of contact between the satellite and the rotor and the curvature are presented. A method for calculating the number of contacts of the satellite teeth with the rotor teeth and of the satellite teeth with the curvature teeth during one revolution of the rotor is proposed. A method of calculating the forces acting at the points of contact of the satellite with the rotor and the curvature is also proposed, as well as a method of calculating the stress in the tooth contact of the interacting components of the mechanism. The results of calculations of forces and stresses in tooth contact in a satellite mechanism consisting of a four-hump rotor and a six-hump curvature are presented. It is shown that the two chambers around the satellite are in the same phase in a certain range of the rotation angle of the rotor, i.e. in the emptying phase or in the filling phase. This results in the value of the force acting on the satellite resulting from the pressure difference being zero. It has also been shown that the most important parameters affecting tooth wear are the pressure difference in the working chambers of the satellite mechanism and the rotor speed.

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“…Literature on gearbox elements failures and their diagnosis are also available, for example in Refs. 59 – 61 . However, it is no less concerned with gears mounted on axles or shafts.…”

Section: Introductionmentioning

confidence: 99%

Influence of geometrical and operational parameters on tooth wear in the working mechanism of a satellite motor

Sliwinski

2023

Sci Rep

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“…EMD decompose any complex signal into a finite number of components, called intrinsic mode functions (IMFs). An IMF is a function with the same number of extrema and zero crossings, whose envelopes are symmetric with respect to zero [15,16].…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

Felix

Martins

Monteiro

et al. 2023

Preprint

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Gearboxes are widely used in various industries such as aircrafts, automobiles, wind turbines, ship industries among others. Due its complex configuration, it is a challenging task to identify fault and failures patterns. Its internal components, such as bearings and gears, have different fault patterns, that can appear in one or in both components. The vibration signals were processed using the Empirical Mode Decomposition (EMD) and the Pearson Correlation Coefficient (PCC) to select the significant IMFs and then 18 features were extract from this IMFs. Four features ranking techniques (ReliefF, Chi-Square, Max Relevance Min Redundancy (mRMR) and Decision Tree) were used in a committee to select the best feature set, among the 10 with the highest rank, that appears at least in 3 of the 4 methods. The new feature set was used as an input to Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN) algorithms. The results showed that the use of the PCC value as a tool for selecting the significant IMFs, combined with the feature committee led to good results for this classification problem. In this case study, the ANN model outperformed the SVM and the RF algorithms, by using only 4 features to achieve 95.42% of accuracy and 6 features to achieve 100% of accuracy.

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“…In the past two decades, intense research has been conducted on tooth gear defect detection from vibration, acoustic, and acoustic emission signals. The studies are based on traditional fault detection methods, such as STFT, CA, WT, WVD, and Hilbert–Huang transform (HHT), followed by processing with machine learning (ML) techniques: SVM (Support Vector Machine), K-NN (K-Nearest-Neighbors), Random Forest, Naïve Bayes, Bayes Net, and NN (Neural Network) [ 11 , 21 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands

Lupea,

Coroian

2024

Sensors

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The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels.

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A Machine Learning Approach for Gearbox System Fault Diagnosis

Cited by 12 publications

References 57 publications

Influence of geometrical and operational parameters on tooth wear in the working mechanism of a satellite motor

Influence of geometrical and operational parameters on tooth wear in the working mechanism of a satellite motor

A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands

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