Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Zhang, Min; Cai, Zhenyu; Cheng, Wenming

doi:10.1155/2018/6209371

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…8 A novel method called multi-scale feature extraction (MFE) and MSVM with particle parameter adaptive (PPA) was proposed for intelligent multiple-fault diagnosis. 9 To diagnose compound faults of locomotive roller bearings accurately, a novel hybrid intelligent diagnosis method was proposed, 10 and the diagnosis results of the compound faults of the locomotive roller bearings verified that the proposed hybrid intelligent method may accurately recognize compound faults. A new method based on use of combined mode functions for selecting the intrinsic mode functions instead of the maximum cross correlation coefficient–based EEMD technique, sandwiched with, convolution neural networks, which were deep neural nets, used as fault classifiers.…”

Section: Introductionmentioning

confidence: 90%

Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model

Wang

2014

Advances in Mechanical Engineering

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Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. Intelligent diagnosis method is an effective method for compound faults of rolling element bearing, and effective fault feature extraction is the key step to decide the intelligent diagnosis result to some extent. The sparse decomposition method could capture the complex impulsive characteristic components of rolling bearing more effectively than the other time–frequency analysis method when compound fault arises in rolling bearing. Based on the self-learning dictionary under different operating states of the device corresponding to the special features modes, an intelligent diagnosis method of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model is proposed in the article. First, characteristic dictionaries of rolling bearing under different operating conditions are extracted by sparse decomposition self-learning method, and state dictionary set of rolling bearing is constructed. Then, the compound fault signals of bearing are transformed into sparse domain using the constructed dictionary set to extract sparse features. At last, the extracted sparse features are used as training and testing vectors of hidden Markov model, and satisfactory intelligent diagnosis results are obtained. The validity of the proposed method is verified by compound faults of rolling element bearing. In addition, the advantages of the proposed method are also verified by comparing with the other feature extraction and intelligent diagnosis methods, and the proposed method provides a feasible and efficient solution for fault diagnosis of rolling bearing compound faults.

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Section: Introductionmentioning

confidence: 90%

Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model

Wang

2014

Advances in Mechanical Engineering

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“…In [19], lifting wavelet packet transform (LWPT) and binary tree system are employed to realize bearing fault diagnosis. In [20], the authors utilized multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) for bearing fault feature extraction and classification, respectively. In [21], trace ratio criterion LDA (TR-LDA) and kNN classifier are used for feature reduction and fault classification to realize multifault severity detection of bearings.…”

Section: Comparisons Of Classification Performance On Cwrumentioning

confidence: 99%

“…Load 0 (400) Load 0 (200) 99.53 MFE and MSVM in [20] Load 0 (840) Load 0 (360) 94.50 TR-LDA2 and kNN classifier in [21] Load 1 (200) Load 1 (600) 98.00 Load 2 (150) Load 2 (650) 97.65 Multiple ANFIS combination in [22] Load 0-3 (300) Load 0-3 (300) 91.33 MKMFA and kNN in [23] Load 0-3 (500) Load 0- Table 2 in [18], which is the average of the ten classes. b e test settings are same with that in G1-G5 using the adjusted spectrum images.…”

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confidence: 99%

Fault Diagnosis of Bearings with Adjusted Vibration Spectrum Images

Qiu

Zhu

et al. 2018

Shock and Vibration

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In order to diagnose bearing faults under different operating state and limited sample condition, a fault diagnosis method based on adjusted spectrum image of vibration signal is proposed in this paper. Firstly, the Davies–Bouldin index (DBI) is employed to select a proper capture focus (CF) and image size, and the spectrum of vibration signal is computed via fast Fourier transformation (FFT) and adjusted according to the average rotating speed. Then, the spectrum is plotted and captured as a two-dimensional (2D) image with the optimized CF and image size. Two-dimensional principal component analysis (2DPCA) is used to reduce the dimension of images, and finally a nearest neighbour method is applied to classify the faults of bearings. Two experiments are carried out to validate the effectiveness of the proposed method. Besides, a further investigation on the effect of spectrum frequency resolution is conducted and a recommended selection method of frequency resolution is given based on the experimental performances. In our method, the training samples could be from only one operating condition, while the testing samples are from all possible operation conditions. All experiment results have demonstrated that the proposed method could achieve high classification accuracy even with very limited training samples.

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A systematic review of diagnosis methods for rolling bearing compound faults: research status, challenges, and future prospects

Li,

Wang,

Yan

et al. 2024

Meas. Sci. Technol.

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Rolling Bearing Compound Fault (RBCF) is characterized by randomness, sequentiality, coupling, and concealment, which is one of the primary causes for unscheduled downtime of rotating machinery. Therefore, timely detecting defects is essential to reduce downtime and ensure safety of equipment. This paper provides a systematic review for the existing applications and developments of diagnosis methods of RBCF since 2004. Categorized as fault mechanism analysis methods based on analytical model, feature extraction methods based on signal processing, and pattern recognition methods based on artificial intelligence, and their diagnostic frameworks are summarized in detail, respectively. The advantages and disadvantages of the reviewed methods are concluded. The challenges and prospects for the diagnosis methods of RBCF are analyzed and discussed in further. This work can offer valuable insights and research inspiration for academic scholars and industry engineers in diagnosing compound faults for rolling bearing.

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Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Cited by 5 publications

References 25 publications

Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model

Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model

Fault Diagnosis of Bearings with Adjusted Vibration Spectrum Images

A systematic review of diagnosis methods for rolling bearing compound faults: research status, challenges, and future prospects

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