Fault diagnosis of rotating machines based on EEMD-MPE and GA-BP

Jin, Tongtong; Cheng, Qiang; Chen, Hu; Wang, Siyuan; Guo, Jinyan; Chen, Chuanhai

doi:10.1007/s00170-021-08159-z

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…In order to improve the accuracy of fault diagnosis, Jin et al [168] proposed an ensemble approach based on EEMD, MPE and GA-BPNN, which could realize the identification of 16 fault patterns. DT is a tree structure, representing a mapping relationship between object attributes and object values.…”

Section: Fault Feature Identification Based On Classical Machine Lear...mentioning

confidence: 99%

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.

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Section: Fault Feature Identification Based On Classical Machine Lear...mentioning

confidence: 99%

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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“…To improve the identification performance of free-conducting particle faults, MPE is extracted to reflect the randomness and mutability of vibration signals. According to the previous researches, there are four parameters should be selected with special care when calculating MPE: the scale factor s, embedding dimension m, delay time τ, and the length of time series N (Jin et al, 2021). It should be noted that setting the initial value of the four parameters is the key to achieving optimal MPE.…”

Section: Feature Extractionmentioning

confidence: 99%

“…(3) The SAWOA is employed to optimize the critical parameters of MPE. In the MPE algorithms, unreasonable parameters setting will make MPE unable to effectively characterize the feature information of the signals (Jin et al, 2021;.…”

mentioning

confidence: 99%

Fault diagnosis of free-conducting particles within GIL based on vibration signals

Duan

Ma²,

Yang³

et al. 2023

Front. Energy Res.

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Accurate quantitative diagnosis of free-conducting particle faults plays an important role in improving the reliability of the gas insulated line (GIL) system. However, the existing fault diagnosis methods cannot accurately identify the free-conducting particle faults with different quantities and sizes. Motivated by this, this paper proposes a novel fault diagnosis method based on vibration signals, which integrates variational mode decomposition (VMD), self-adapting whale optimization algorithm-multiscale permutation entropy (SAWOA-MPE), and deep forest (DF). First, the raw vibration signals of free-conducting particle faults are decomposed via VMD, and the decomposed signals are reconstructed based on the correlation degree. Afterwards, SAWOA is employed to optimize the critical parameters of MPE, and the optimized MPE is further utilized to extract the fault features of the reconstructed signals. Finally, the extracted feature vectors are trained and tested to construct a valid DF classification model that identifies the free-conducting particle faults. The experimental results indicate that the identification accuracy of the proposed method can reach 99.5%. Moreover, comparative tests based on various feature vector extraction methods and classification models further validate the superiority of the proposed method.

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“…ChunYao L [5] used the particle swarm algorithm (particle swarm optimization, PSO) to optimize the BP model, which greatly improved the fault identification rate. By comparing GA genetic algorithm (genetic algorithm, GA) -BP with BP algorithm, Tongtong J [6] et al concluded that the former has fewer iterative steps and could achieve the preset target faster compared with BP neural network fitting data. Sparrow search algorithm(parrow search algorithm, SSA) compared with other group algorithm higher efficiency, faster solution efficiency, but also into the local optimal solution, so this paper adopts a improved sparrow search algorithm (improved sparrow search algorithm, ISSA) the algorithm by BP neural network weights and threshold improved to achieve faster convergence and more accurate results.…”

Section: Introductionmentioning

confidence: 99%

Wind power gearbox fault diagnosis of BP neural network based on improved Sparrow algorithm

Li,

Fan

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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It is difficult to identify the faults on the surface and root of the fan gearbox, resulting in low diagnostic accuracy. As an important part of the wind turbine, the abnormal vibration of the gear is easy to cause great losses, in order to improve the specific fault identification rate and reduce the fault. The following aspects are used to improve the sparrow algorithm. First, the homogeneous distribution Tent chaotic model is introduced to initialize the population distribution to improve the uniformity of the population distribution, then the Gaussian variation is used to add the random distribution to the newly generated individuals, and finally the adaptive weight is used to determine the value of the adjustment weight from the adaptation according to the population search state. The optimized sparrow algorithm can avoid falling into the local optimal solution and improve the convergence speed. By using the final conclusion of the improved sparrow algorithm, the accuracy of fault detection can be improved.

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Fault diagnosis of rotating machines based on EEMD-MPE and GA-BP

Cited by 12 publications

References 33 publications

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Fault diagnosis of free-conducting particles within GIL based on vibration signals

Wind power gearbox fault diagnosis of BP neural network based on improved Sparrow algorithm

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