Adaptive vibration condition monitoring technology for local tooth damage in gearboxes

Tooth pitting is a common failure mode of a gearbox. Many researchers investigated dynamic properties of a gearbox with localized pitting damage on a single gear tooth. The dynamic properties of a gearbox with pitting distributed over multiple teeth have rarely been investigated. In this paper, gear tooth pitting propagation to neighboring teeth is modeled and investigated for a pair of spur gears. Tooth pitting propagation effect on time-varying mesh stiffness, gearbox dynamics and vibration characteristics is studied and then fault symptoms are revealed. In addition, the influence of gear mesh damping and environmental noise on gearbox vibration properties is investigated. In the end, 114 statistical features are tested to estimate tooth pitting growth. Statistical features that are insensitive to gear mesh damping and environmental noise are recommended.

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“…Some researchers [4][5][6][7] created man-made tooth pitting on gears to experimentally explore fault symptoms of a gearbox. For example, in Ref.…”

Section: Introductionmentioning

confidence: 99%

Spur Gear Tooth Pitting Propagation Assessment Using Model-based Analysis

Liang

Liu

Pan

et al. 2017

Chin. J. Mech. Eng.

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“…This applies especially to the diagnosing of the development of gear tooth pitting and fatigue cracks at the tooth base. It is assumed in fact that even 60% of the gear damages are caused by fatigue damage of teeth [2] referred in the literature as local damages to the gears [3]. On the other hand, according to the report of Allianz VersicherungsAktiengesellschaft [4], fatigue damage to the teeth and their forced fracture are responsible for 90% damage of gears.…”

Section: Introductionmentioning

confidence: 99%

“…They were described in the classic works of McFadden [12] and McFadden and Smith [17], as well as Randall and Sweeney [18] concerning the possibility of the location of the damaged tooth. Among the more recent works, the work of Gelman et al [2] and review works of Feldman [19,20] on the Hilbert transform should be mentioned. The idea behind these methods was to get through the demodulated time signal to the damaged area, tooth gear, introducing of local disturbances in the signal.…”

Section: Introductionmentioning

confidence: 99%

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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“…Until now, some researchers noticed that PCA may be used for reduction of data dimensionality [8,10,14]. The PCA technique has been applied together with other statistical techniques for time domain data (like RMS, kurtosis, skewness, peak etc), time-frequency parameters (for example wavelet coefficient [8,10,11]) and other [9,16]. PCA has been applied recently also in data processing for time varying systems (for structural health monitoring [15] and for planetary gearbox diagnosis [19]).…”

Section: Introductionmentioning

confidence: 99%

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

Zimroz¹,

Bartkowiak²

2011

J. Phys.: Conf. Ser.

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Abstract. Spectral analysis is well-established analysis of vibrations used in diagnostics both in academia and industry. In general, one may identify components related to particular stages in the gearbox and analyze amplitudes of these components with a simple rule for decisionmaking: if amplitudes are increasing the condition becomes worse. However, usually one should analyze not single amplitude but at least several components, but: how to analyze them simultaneously? We have provided an example (case study) for planetary gearboxes in good and bad conditions (case B and case A). As diagnostic features we have used 15 amplitudes of spectral components related to fundamental planetary mesh frequency and its harmonics. Using Principal Component Analysis (PCA), it has been shown that amplitudes don't vary in the same way; change of condition affects not only amplitudes of all components in that sense, but also relation between them. We have investigated geometry of the data and it has been shown that the proportions of the explained total inertia of the three data sets ("good", "bad" and mixed good/bad) are different. We claim that it may be a novel diagnostic approach to employ multidimensional analysis for accounting not only directly observed values but also interrelations both within and between the two groups of data. Different structure of the data is associated with different condition of the machines and such assumption is specified for the first time in the literature. Obviously it requires more studies.

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Adaptive vibration condition monitoring technology for local tooth damage in gearboxes

Cited by 45 publications

References 3 publications

Spur Gear Tooth Pitting Propagation Assessment Using Model-based Analysis

Spur Gear Tooth Pitting Propagation Assessment Using Model-based Analysis

Model-based detection of local defects in gears

Investigation on Spectral Structure of Gearbox Vibration Signals by Principal Component Analysis for Condition Monitoring Purposes

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