Dynamic transmission error measurements of a drive train

Brecher, Christian; Gorgels, Christof; Hesse, Joachim Jens; Hellmann, Martin

doi:10.1007/s11740-011-0310-5

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…In a first preprocessing step, we used the synchronously measured angular data of the shafts to calculate the transmission error Δ ( ) of the pair of test gears (see Eq. (1), (Brecher, Gorgels, Hesse, & Hellmann, 2011;Niemann & Winter, 2003)) with 1,2 as the teeth numbers and Δ 1,2 ( ) as the rotation angles of pinion and wheel.…”

Section: Data Preprocessingmentioning

confidence: 99%

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Sendlbeck

Fimpel

Siewerin

et al. 2021

IJPHM

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Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.

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Section: Data Preprocessingmentioning

confidence: 99%

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Sendlbeck

Fimpel

Siewerin

et al. 2021

IJPHM

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“…Nowadays, the need for dynamic TE measurement grows faster because dynamic TE has a closer relationship with gear whine noise. ere are three methods suitable for measuring dynamic TE: one based on optical encoders [143,144], another based on vibrometers [145], and the last based on accelerations [142,[146][147][148].…”

Section: Transmission Error Measurementmentioning

confidence: 99%

Classifying, Predicting, and Reducing Strategies of the Mesh Excitations of Gear Whine Noise: A Survey

Sun

Liu

et al. 2020

Shock and Vibration

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Gear whine noise has attracted increasing attention from researchers in both the academe and the industry over the past two decades. The wide range of research topics demonstrates that there is a huge technical challenge in understanding the source-path-receiver mechanisms deeply and predicting the gear whine noise precisely. Thoroughly understanding the sources of gear whine noise is the first step to solving this issue. In this paper, the authors summarize a certain number of published articles regarding the sources of gear whine noise. The excitations of gear whine noise are classified into three groups: transmission error along the line of action direction, frictional excitations along the off-line of action direction, and shuttling excitation along the axial direction. The mechanisms, characteristics, predicting approaches, measuring methods, and decreasing strategies for these excitations are summarized. Current research characteristics and future research recommendations are presented at the end.

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“…In fact, TE measurement has traditionally been limited to gear pairs tested under extremely light-load conditions, primarily to verify gear manufacturing accuracy. 11 In one study, 12 a series of production manual transmission TE measurements were represented, showing how to measure TEs using an encoder under quasi-static conditions and an accelerometer under dynamic conditions. As for TE measuring instruments, it can be confirmed that there are three main types: optical rotary encoders, rotary laser vibration meters, and linear accelerometers.…”

Section: Introductionmentioning

confidence: 99%

An accurate measurement method of gear transmission error based on a double-angle measurement device

Cao,

Qiu,

Gong

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, a geometric model of a grating and reading head was established, where the relationship between the angle deviation, which was compensated for by double reading heads, and the actual angle is deduced. A new measurement method was proposed by applying the clock pulse counting method to the measurement of the transmission error of the gear pair input-and-output angle encoder while taking into account the gear pair’s transmission error. To demonstrate the feasibility of this measurement method, a transmission error test bench of involute gear pairs was built. Based on the output gear, the counted pulses were divided into integer pulses and non-integer pulses, and a transmission error curve was obtained by processing the pulses. The transmission errors of a helical gear with untrimmed angles and a gear pair with trimmed angles were also measured. Compared with the untrimmed gear pair, the first-order amplitude of the trimmed gear pair was reduced by 25.25%, 0.79%, and 16.9%, but other whole-order full-harmonic amplitudes were excited. However, the peak value of the single tooth surface of the trimmed gear pair was generally less than that of the untrimmed gear pair. The peak transmission error and the amplitude of the tooth harmonic frequency had good repeatability, within 16%, which verified the feasibility of the transmission error test method. The measurement precision reached 1 arcsecond in the tooth frequency.

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Dynamic transmission error measurements of a drive train

Cited by 9 publications

References 3 publications

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Classifying, Predicting, and Reducing Strategies of the Mesh Excitations of Gear Whine Noise: A Survey

An accurate measurement method of gear transmission error based on a double-angle measurement device

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