Fault Diagnosis of Rotating Machinery Based on the Multiscale Local Projection Method and Diagonal Slice Spectrum

Lv, Yong; Yuan, Rui; Shi, Wei

doi:10.3390/app8040619

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…However, this method often ignores the local structure underlying data, resulting in the loss of potential information from such structures. Locality-preserving projection (LPP) is a manifold learning method that maintains the local structure of data and can restore a low-dimensional manifold structure from high-dimensional sample data [ 36 , 37 , 38 ]. At present, scholars use LPP in combination with PCA [ 39 , 40 ], but the statistical model established by this combination method is static; that is, it assumes that the current process is time-invariant.…”

Section: Process Monitoring Based On Dlppcamentioning

confidence: 99%

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

Wang

Tong

et al. 2023

Sensors

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Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey–Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment’s historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method.

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Section: Process Monitoring Based On Dlppcamentioning

confidence: 99%

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

Wang

Tong

et al. 2023

Sensors

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“…where ϕ ∈ ϕ neigbor i . 7After determining the representative prototypes of category c, according to Formula (17), AnYa type fuzzy rules belonging to each category are constructed, where N c is the number of prototypes in p c .…”

Section: Offline Training Stagementioning

confidence: 99%

“…However, due to the presence of noise, the collected fault signal may be submerged. In order to enhance the signal, filters are often used, such as minimum entropy deconvolution (MED), maximum correlation kurtosis deconvolution (MCKD), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) [15][16][17]. However, the above algorithm is not adaptive, and misdiagnosis may occur.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

Guo

Wang

et al. 2019

Entropy

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The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

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“…Gear and bearing are very important parts in mechanical systems, because their working conditions can directly affect safety and stable operations. Therefore, it is of great significance to detect and diagnose their operating states [1][2][3][4]. The main denoising algorithms aim at one dimensional time domain vibration signals in the field of current mechanical signal noise reduction methods [5].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

Xiao

2019

Applied Sciences

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Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.

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Fault Diagnosis of Rotating Machinery Based on the Multiscale Local Projection Method and Diagonal Slice Spectrum

Cited by 4 publications

References 35 publications

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

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