Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lina; Zhang, Lin; Liu, Xinhua

doi:10.3390/app6070199

Cited by 35 publications

(21 citation statements)

References 36 publications

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“…Ahmet et al (2017) proposed an improved version of FOA and showed through experiment that the improved version of FOA was more equal and fairer in terms of screening the solution space. Xu et al (2016) proposed an IFOA and illustrated its effectiveness and superiority through a comprehensive comparison among five typical algorithms. Han et al (2017) developed a novel FOA with trend search and coevolution and showed experimentally that the novel FOA had higher robustness.…”

Section: Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Liu

Wang

et al. 2019

Front. Mater.

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This paper aims to improve control performance for a magnetorheological damper (MRD)-based semi-active seat suspension system. The vibration of the suspension is isolated by controlling the stiffness of the MRD using a proportion integration differentiation (PID) controller. A new intelligent method for optimizing the PID parameters is proposed in this work. This new method appropriately incorporates particle swarm optimization (PSO) into the PID-parameter searching processing of an improved fruit fly optimization algorithm (IFOA). Thus, the PSO-IFOA method possesses better optimization ability than IFOA and is able to find a globally optimal PID-parameter set. The performance of the PID controller optimized by the proposed PSO-IFOA for attenuating the vibration of the MRD suspension was evaluated using a numerical model and an experimental platform. The results of both simulation and experimental analysis demonstrate that the proposed PSO-IFOA is able to optimize the PID parameters for controlling the MRD semi-active seat suspension. The control performance of the PSO-IFOA-based PID is superior to that of individual PSO-, FOA-, or IFOA-based methods.

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Section: Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Liu

Wang

et al. 2019

Front. Mater.

Self Cite

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“…Therefore, the image of a surface with multiple peaks and valleys is prone to producing ambiguous gray scale information, resulting in an inability to identify the convexity and concavity of the surface. The wavelet denoising algorithm is adopted to eliminate quantization errors [35], which can maintain the details of the gray information and smooth the discontinuity of gray values. By setting an appropriate wavelet decomposition level and quantization threshold for decomposition coefficients, the curves of gray scale and gray gradient were smoothed, as shown in Figure 3b and Figure 4, respectively.…”

Section: The Ambiguous Gray Scalementioning

confidence: 99%

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Shi

Zheng

et al. 2019

Applied Sciences

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Featured Application: Applying on mechanical engineering. Abstract: In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.Appl. Sci. 2019, 9, 591 2 of 16 wear particle surfaces based on a photometric stereo, and obtained the surface topographies of wear particles for further feature-based wear particle identification. Tang et al. [9] used the 3D reconstruction method based on the shape from a focus to determine the grinding wheel surface topography.Three-dimensional reconstruction is the process of recovering 3D shapes of objects from single or multiple images [10][11][12], and the shape from shading (SFS) method is considered to be one of the fastest and most efficient 3D reconstruction methods [13][14][15]. Therefore, the SFS-based reconstruction method has attracted attention from numerous researchers. Maurer et al. [16] used a method that combined SFS and stereo to establish the functional energy with a detail-preserving anisotropic second-order smoothness term, and estimated the depth of the object surface. Lu et al. [17] proposed a Lambert-Phong hybrid model algorithm, and obtained the coordinate information of the highlighted region of the droplet surface by the mask regions with convolutional neural network (R-CNN). Furthermore, they used the Taylor expansion and Newton iteration method in the reflection model to obtain the heights of all the positions. Sun et al. [18] proposed a new roughness evaluation method based on the complex wavelet enhanced SFS transform, and recovered the 3D topography of a milling surface using the 3D models that were developed from the digital image. Zhu et al. [19] proposed a novel method that uses Fourier transform to fuse the depth from focus (DFF) and SFS, and achieved the reconstruction of the tool wear image in addition to ensuring the continuity of the depth map. Yang et al. [20] used the SFS algorithm to reconstruct the 3D shapes of the welding seam, and extracted the curvature information as the feature vector of the ...

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“…Although DTCWT reduces the frequency mixing to some extent, the frequency mixing still inevitably appears in the sub-bands reconstructed by DTCWT. It is more common to use wavelet threshold noise reduction to reduce frequency mixing [24,25]. Shao et al obtained the main frequency component signals of each sub-band by EMD, and successfully overcame the frequency mixing of DTCWT.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

Zhang

Zhang³

et al. 2018

Entropy

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Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

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Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Cited by 35 publications

References 36 publications

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

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