Gear Tooth Surface Damage Fault Detection Using Induction Machine Stator Current Space Vector Analysis

Kia, Shahin Hedayati; Henao, H.

doi:10.1109/tie.2014.2360068

Cited by 99 publications

(42 citation statements)

References 28 publications

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“…4. At the same time, it is worth noting the increased level of the gear signal around the half of the shaft rotation (gear teeth [15][16][17][18][19]. This reflects to the disturbances related to cooperation of the wheel shaft that may be caused for example by manufacturing errors of the wheel (e.g., eccentricity) or the inaccuracy of installation the gear on the shaft.…”

Section: Local Meshing Plane In Diagnosing Tooth Crackmentioning

confidence: 99%

“…In [15] the interesting method of diagnosis of early stages gear fault detection based on stator current analysis was presented. The method is based on the fact that in case of gear local faults additional components are generated in the mechanical torque that could be seen in the stator current space vector instantaneous frequency (SCSVIF) spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Model-based detection of local defects in gears

Mączak¹,

Jasiński²

2017

Arch Appl Mech

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The paper is presenting a methodology for developing model-based method of gear fault diagnostics. First the simulation model of the helical gearbox is discussed allowing analysis of the phenomena taking place during teeth mating in the presence of manufacturing and assembly errors. It includes observation of influence of errors on the generated signals. The model was initially used to analyze the teeth contact in the presence of pitch errors and later to verify the sensitiveness of proposed diagnostic methods and their availability to detect the fatigue damages of teeth. The common feature of discussed approach is the direct use of time signal processing algorithms, and in contrary to the methods based on spectral analysis it allows precise localization of gear defects like pitting and tooth fracture associating them to the particular pinion or gear teeth. Their advantage is the simplicity and speed of action that is of great significance for the implementation in the autonomous transmission diagnostic systems and diagnostic systems working online. Presented methods of signal processing were first tested on a simulation model of the gear assembly and later verified during the experiments on a back-to-back test stand.

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Section: Local Meshing Plane In Diagnosing Tooth Crackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based detection of local defects in gears

Mączak¹,

Jasiński²

2017

Arch Appl Mech

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“…Besides, by considering variable pitch angle at the propeller's blades an additional control input can be generated, allowing for a more dexterous manipulation of the ship's motion [7]- [8]. The results of this article contribute to developing a more efficient control system for an electric ship's propulsion and to managing optimally the associated power electronics [9]- [11]. Actually, in the present article, a nonlinear optimal control approach is developed for the propulsion system of electric ships, comprising a three-phase induction motor, a drivetrain and a propeller.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for ship propulsion with the use of an induction motor and a drivetrain

et al. 2018

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Abstract.A nonlinear optimal (H-infinity) control method is proposed for an electric ship's propulsion system that consists of an induction motor, a drivetrain and a propeller. The control method relies on approximate linearization of the propulsion system's dynamic model using Taylor-series expansion and on the computation of the state-space description's Jacobian matrices. The linearization takes place around a temporary equilibrium which is recomputed at each time-step of the control method. For the approximately linearized model of the ship's propulsion system, an H-infinity (optimal) feedback controller is developed. For the computation of the controller's gains an algebraic Riccati equation is solved at each iteration of the control algorithm.The stability properties of the control method are proven through Lyapunov analysis,

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“…Although incipient faults such as wear in gears are those which originate crucial failures in gearboxes transmission systems, this kind of fault has not been totally detected until the appearance of a critical breakdown. In this sense, the most critical faults are related to the appearance of gear teeth irregularities; such faults are: tooth breakage, chipped tooth, root crack, spalling, pitting and tooth surface damage, and due to their typical appearance, these faults have been widely addressed [11]- [13]. Hence, the appearance of faults in a gearbox transmission system affects its proper operation causing mainly the occurrence of vibrations, the increasing of noise besides that the temperature in the mechanical system is affected [14]- [15].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Saucedo-Dorantes

Delgado-Prieto

Osornio-Ríos

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

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Gear Tooth Surface Damage Fault Detection Using Induction Machine Stator Current Space Vector Analysis

Cited by 99 publications

References 28 publications

Model-based detection of local defects in gears

Model-based detection of local defects in gears

Nonlinear optimal control for ship propulsion with the use of an induction motor and a drivetrain

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

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