Vibration-based updating of wear prediction for spur gears

Feng, Ke; Borghesani, Pietro; Smith, Wade A.; Chin, Zhan Yie; Ren, Jinzhao; Peng, Zhongxiao

doi:10.1016/j.wear.2019.01.017

Cited by 123 publications

(62 citation statements)

References 19 publications

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“…According to the principle of polynomial fitting, 2 χ is the smallest after fitting. During the deceleration and declining stage, 2 χ is the smallest under 4-term polynomial fitting.…”

Section: ) Case2:same Load--different Backlashesmentioning

confidence: 99%

“…According to the principle of polynomial fitting, 2 χ is the smallest after fitting. During the deceleration and declining stage, 2 χ is the smallest under 4-term polynomial fitting. In the accelerated rising phase, the corresponding 2 χ is the smallest under 6-term polyphase fitting.…”

Section: ) Case2:same Load--different Backlashesmentioning

confidence: 99%

“…Therefore, many scholars have carried out research and achieved some achievements. With the rapid development of mechanical automation, with the increase in power and speed, friction [2,3], scratches [4,5], pitting, broken teeth [6] and misalignment [7,8] of gears have become major concerns of scholars [9]. Research of gear fault diagnosis is of extreme significance to industrial production and social life.…”

Section: Introductionmentioning

confidence: 99%

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Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Yang¹,

Liu²,

Deng³

2020

IEEE Access

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Motor Current Signal Analysis (MCSA) is widely used in the monitoring of electric motors, but few researchers applied it in the transmission system because of the fault information loss and aliasing of current signals. The purpose of the research is to analyze the influence of the gear backlash under different size effect on the current signal from the permanent magnet synchronous motor (PMSM). And it supplies a convenient route to detect the gear backlash more economy. In most cases, the current of PMSM is usually changed with the operating states, which may relate with rotor speed, load and gear backlash. So, it is important to extract the sensitive features under different states. Here, the stable speed and variable speed states are considered at first. At the steady stage, multiple features are extracted from current signals under variable backlash. And fisher discriminant analysis (FDA) is introduced to evaluate and select the most sensitivity features for backlash are selected. Then, features at variable speed states are extracted. Because the transient change of the current frequency and phase adjustable, it is very difficult to obtain the steady state response characteristic. In this paper, the inverse of the time difference between the positive peaks of the signal at this phase is utilized as the characteristic index describing phase. Furthermore, the polynomial principle is combined to enhance the characteristics of the extracted features. Therefore, the mapping relationship between the backlash and the current signal of the servo motor is established under different speed stages. The results show that by monitoring the motor current, it is feasible and effective to distinguish the different backlash of the meshing gear. INDEX TERMS MCSA, gear backlash, servo motor, current signal, feature extraction.

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“…According to the principle of polynomial fitting, 2 χ is the smallest after fitting. During the deceleration and declining stage, 2 χ is the smallest under 4-term polynomial fitting.…”

Section: ) Case2:same Load--different Backlashesmentioning

confidence: 99%

Section: ) Case2:same Load--different Backlashesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Yang¹,

Liu²,

Deng³

2020

IEEE Access

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“…EB vH (4) in which, E and v are the Young's modulus and the Poisson's ratio, respectively; B is the teeth width, and Hp is the thickness on the pitch circle. Eq.…”

Section: Figure 2 Schematic Diagram Of a Gear Tooth Cantilever Beammentioning

confidence: 99%

“…The faults may generate serious economic losses and even casualties. Specifically, damages (e.g., pitting, cracking, and spalling) on the gear teeth are the main causes of system failure, and they usually impact the dynamic characteristics of gear devices [2][3][4][5]. Background noise often occurs in actual fault detection.…”

Section: Introductionmentioning

confidence: 99%

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Shi

Wen

Pan

et al. 2020

Preprint

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Gear fault diagnosis has been a vital technology to enhance the reliability and reduce the maintenance cost of gear systems. Tooth spalling is one of the most destructive surface failure models of the gear faults. Revealing the dynamic characteristics of a gear system with spalling fault and extracting the fault feature are the premise and basis for effective fault diagnosis. Previous studies have mainly concentrated on the spalling damage on a single gear tooth, but the spalling distributed over double teeth which usually occurs in practical engineering problems is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, the relative position of two faulty teeth, and the rotational speed. The method based on the Variational Mode Decomposition (VMD) and the Fast Kurtogram (FK) is proposed to extract the features of the double-teeth spalling fault. First, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the VMD, and the IMFs with strong correlation are summed as a reconstructed signal. The reconstructed signal is then filtered by an optimal band-pass filter based on the FK. Combined with envelope spectrum analysis, the feature extraction ability of the proposed method is compared with that of the original FK method and the method based on the Empirical Mode Decomposition and the FK, respectively. The results indicate that the proposed dynamic model and fault feature extraction method can provide a theoretical basis for spalling defect diagnosis of gear systems.

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Fault Trending and Prognostics

2021

Vibration–based Condition Monitoring

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Vibration-based updating of wear prediction for spur gears

Cited by 123 publications

References 19 publications

Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Fault Trending and Prognostics

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