Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance

Ai, Yanting; Guan, Jian; Fei, Chengwei; Jun, Tian; Zhang, Fengling

doi:10.1016/j.ymssp.2016.11.019

Cited by 87 publications

(43 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature extraction can effectively reduce the uncertainty in vibration sensing data. Common feature extraction methods include information entropy [33][34][35], time domain analysis [36,37], empirical mode decomposition [38][39][40], and wavelet packet analysis [41,42]. Compared to the information entropy method and the empirical mode decomposition method, time domain analysis is less affected by the interruption of time-frequency signals, the steps of feature extraction are relatively simple, and different time domain features contain different information in the vibration signal.…”

Section: Feature Extraction Methods Based On Vibration Sensing Datamentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

View full text Add to dashboard Cite

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

show abstract

Section: Feature Extraction Methods Based On Vibration Sensing Datamentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Therefore, these indicators are handled with the entropy weighting method for a more comprehensive assessment of the ANN. Based on the fundamental principles of information theory, information is a measurement of the degree of order for a given system, and the entropy is a measurement of the degree of disorder [32]. The entropy weighting method serves as a mathematic method and considers the information provided by each factor [33].…”

Section: The Description Of the Entropy Weighting Methodsmentioning

confidence: 99%

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

View full text Add to dashboard Cite

Event-based runoff-pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff-pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

show abstract

“…When the bearing component's surface is locally damaged, the high-frequency impact response between components during the operation will be excited [7,8] and the vibration signals exhibit an amplitude modulation phenomenon that combines the characteristic frequency of the bearing defect with the structural resonances [9]. Therefore, as a sensitive and effective method [10], vibration measurements are widely used to detect bearing defects in the fields of aviation [11], transportation [12], energy [13], and other fields. Among many diagnostic methods, the envelope detection (ED) method is one of the most commonly used and effective methods in vibration-based bearing fault diagnosis [14,15], which was presented by Mechanical Technology Inc. in the early 1970s [16] and was originally called the high frequency resonance technique [17].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Wang²,

Wang³

et al. 2020

Sensors

View full text Add to dashboard Cite

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

show abstract

Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance

Cited by 87 publications

References 38 publications

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Contact Info

Product

Resources

About