M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems

Qin, Yi; Mao, Yongfang; Tang, Baoping; Wang, Yi; Chen, Haizhou

doi:10.1016/j.ymssp.2019.106298

Cited by 73 publications

(31 citation statements)

References 40 publications

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“…The orthogonal basis wavelet transform for signal decomposition is adopted 49 . Discrete wavelet transform is composed of a series of parameters.…”

Section: Related Workmentioning

confidence: 99%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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Summary Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.

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“…The orthogonal basis wavelet transform for signal decomposition is adopted 49 . Discrete wavelet transform is composed of a series of parameters.…”

Section: Related Workmentioning

confidence: 99%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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“…How to analyze, process, and identify these signals is very important for judging the working state of rotating machinery and fault diagnosis of equipment [4]. It is very difficult to analyze and process the sensor signal directly, which is bound to cause great difficulties to the mechanical condition monitoring and fault diagnosis [5].…”

Section: Introductionmentioning

confidence: 99%

“…Various traditional modern signal processing methods, such as empirical mode decomposition (EMD) [6], wavelet transform [7,8], adaptive filter [9,10], Kalman filter [11,12], and mathematical morphology analysis [13,14], have been widely used in vibration signal analysis. Qin et al [5] proposed a novel M-band flexible wavelet transform for identifying the underlying fault features in measured signals. Wang et al [2] proposed a step-by-step compound faults diagnosis method for equipment based on majorizationminimization (MM) and constraint sparse component analysis (SCA).…”

Section: Introductionmentioning

confidence: 99%

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao

Zhao

2020

Mathematical Problems in Engineering

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When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

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“…Gearbox is one of the key components in rotating machinery, and the failure rates of its components are respectively about: gear 60%, bearing 19%, shaft 10%, case 7%, tight solid 3%, oil seal 1% [ 1 ]. Evidently, gears have high fault probabilities because of their complex structures and harsh operating conditions [ 2 , 3 ]. Gear pitting is one of the most common failure modes of gears.…”

Section: Introductionmentioning

confidence: 99%

Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN

Qin

Wang

2020

Sensors

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To accurately and quantitatively detect the gear pitting of different levels on the actual site, this paper studies a new vision measurement approach based on a tunable vision detection platform and the mask region-based convolutional neural network (Mask R-CNN). The shooting angle can be properly set according to the specification of the target gear. With the obtained sample set of 1500 gear pitting images, an optimized deep Mask R-CNN was designed for the quantitative measurement of gear pitting. The effective tooth surface and pitting was firstly and simultaneously recognized, then they were segmented to calculate the pitting area ratio. Considering three situations of multi-level pitting, multi-illumination, and multi-angle, several indexes were used to evaluate detection and segmentation results of deep Mask R-CNN. Experimental results show that the proposed method has higher measurement accuracy than the traditional method based on image processing, thus it has significant practical potential.

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M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems

Cited by 73 publications

References 40 publications

Network traffic prediction method based on wavelet transform and multiple models fusion

Network traffic prediction method based on wavelet transform and multiple models fusion

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN

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