An Improved Fault Diagnosis Method of Rotating Machinery Using Sensitive Features and RLS-BP Neural Network

Self Cite

In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. Cohesion evaluation algorithm selects high sensitivity feature parameters from time and frequency domain in high-dimensional vectors to construct low-dimensional feature vectors. The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors. An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled situations. To demonstrate the effectiveness of the proposed method, simultaneous fault diagnosis experiments are conducted via wind turbine drivetrain diagnostics simulator (WTDDS). The experiment results show that the proposed method has better overall performance compared with conventional BP-MLL algorithm and some other learning algorithms.

Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP‐MLL for Rotating Machinery

Zhang

Yang

Huang

et al. 2021

Self Cite

“…where θ (1) , θ (2) , • • • , θ (k) are the parameters of the model and Oj is the final output result.…”

Section: Bidirectional Long-and Short-term Memory Neuralmentioning

confidence: 99%

“…As a commonly used power transmission device in mechanical equipment, bearings are widely used in industrial production [1][2][3]. Its operation status will directly determine the useful performance of the equipment [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

You

Chen

Liu

et al. 2021

The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

“…However, the large number of characteristic parameters incurs the following two problems: (1) fault features may not be accurately extracted due to the random components in the signal; (2) large dimension data enhance the modeling difficulty [37]. In this paper, cohesion evaluation is applied to select the sensitive features, which can reserve sensitive features and remove insensitive features by evaluating the cohesion of each feature [38].…”

Section: Sensitive Features Selectionmentioning

confidence: 99%

Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions

Wan

Yang

Huang

2020

Self Cite

In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction. Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with. This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems. In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions. The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.