An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

Wan, Lanjun; Gong, Kun; Zhang, Gen; Yuan, Xinpan; Li, Changyun; Deng, Xiaojun

doi:10.1109/access.2021.3063929

Cited by 73 publications

(40 citation statements)

References 45 publications

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“…2. The detailed explanations of parameters can be found in [60], where numTrees represents the number of decision trees in RF. Increasing the value of numTrees can significantly improve the classification accuracy of RF.…”

Section: Experimental Results and Analysis A Experimental Datasetmentioning

confidence: 99%

“…To evaluate the effectiveness of the proposed MO-TLBO-RF, four different RF algorithms, i.e., Spark-RF [61], Spark-IRF [60], gcForest [62], and MO-TLBO-RF are used for fault diagnosis model training with DATA A, respectively. Spark-RF is an RF algorithm provided by Spark MLlib, Spark-IRF is an improved RF algorithm based on sub-forest optimization and it is implemented with Spark, and gcForest is a latest available open-source ensemble learning model.…”

Section: Model Training and Verification 1) Comparison Of Rf Irf Gcforest And Mo-tlbo-rfmentioning

confidence: 99%

“…Spark-RF is an RF algorithm provided by Spark MLlib, Spark-IRF is an improved RF algorithm based on sub-forest optimization and it is implemented with Spark, and gcForest is a latest available open-source ensemble learning model. In this experiment, the parameter settings of Spark-RF are listed in Table 2, the parameter settings of Spark-IRF can be found in [60], and the key parameter settings of gcForest are as follows: the number of estimators in each cascade layer is set to 4, the number of decision trees in each estimator is set to 100, and the type of the predictor concatenated to the deep forest is specified as "forest".…”

Section: Model Training and Verification 1) Comparison Of Rf Irf Gcforest And Mo-tlbo-rfmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Wan¹,

Gong

Zhang

et al. 2021

IEEE Access

Self Cite

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Ensemble learning has been widely used in various fields. Still, too many base classifiers will affect the classification time of the ensemble classifier under the big data environment, while reducing base classifiers will affect the classification accuracy of the ensemble classifier. Therefore, the multi-objective teaching-learning-based optimization (MO-TLBO) algorithm is used to carry out ensemble pruning of random forest (RF) to improve the classification accuracy and speed of RF. MO-TLBO algorithm aims at maximizing classification accuracy and minimizing classification time, and it can find a sub-forest with higher classification accuracy and faster classification speed. In addition, considering the vast computational time of ensemble pruning of RF via MO-TLBO algorithm under the big data environment, a vote set is constructed to improve the fitness evaluation process. In the Spark platform, the RF improved by the MO-TLBO algorithm (MO-TLBO-RF) is parallelized based on data parallelism. The Shuffle optimization strategy is proposed to reduce the number of Shuffles in the execution of parallel MO-TLBO-RF. The proposed MO-TLBO-RF is applied to rolling bearing fault diagnosis. The experimental results show that the algorithm can obtain an RF with high fault diagnosis accuracy and fast fault diagnosis speed. The results also prove that the ensemble pruning time can be greatly reduced via the vote set and parallelization of MO-TLBO-RF. INDEX TERMS Ensemble pruning, multi-objective TLBO algorithm, parallelization, random forest, Spark.

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Section: Experimental Results and Analysis A Experimental Datasetmentioning

confidence: 99%

Section: Model Training and Verification 1) Comparison Of Rf Irf Gcforest And Mo-tlbo-rfmentioning

confidence: 99%

Section: Model Training and Verification 1) Comparison Of Rf Irf Gcforest And Mo-tlbo-rfmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Wan¹,

Gong

Zhang

et al. 2021

IEEE Access

Self Cite

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show abstract

“…L.J. Wan [ 19 ] proposed a high-efficiency rolling bearing FD method based on Spark and improved random forest, which can increase the detection speed and obtain a higher accuracy. Ma, S.L.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

Tang

et al. 2021

Sensors

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It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

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“…C.He [7] et al wavelet packet transform and improved features in combination Fisher feature extraction methods, and then using the multi-RVM classification task. L.J.Wan [8] et al proposed a method for rapid parallel construction of decision tree similarity matrix based on Spark and optimized sub-forest IRF algorithm to realize bearing fault diagnosis. Y.H.Cheng [9] et al proposed fault diagnostics of rolling bearings using feature fusion based BP, RBF and PNN neural networks.…”

Section: Introductionmentioning

confidence: 99%

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

DeChen¹,

Li²,

Huang³

2021

Preprint

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Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

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An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

Cited by 73 publications

References 45 publications

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Ensemble Pruning of RF via Multi-Objective TLBO Algorithm and Its Parallelization on Spark

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

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