Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Jiang, Fan; Zhu, Zhencai; Li, Wei; Xia, Shixiong; Zhou, Gongbo

doi:10.21595/jve.2017.18859

Cited by 10 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is only one article found on shaft application using VMD and no article found for belt and blades using VMD methods. This has been studied by Jiang et al where they using VMD and SVM for vertical-shaft load lifting monitoring [42].…”

Section: Other Applicationsmentioning

confidence: 99%

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

et al. 2019

View full text Add to dashboard Cite

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.

show abstract

Section: Other Applicationsmentioning

confidence: 99%

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

et al. 2019

View full text Add to dashboard Cite

show abstract

“…It is expressed as the probabilistic expression p(y = j/x), where x is the input sample and the corresponding label is y, p is the probability of sample j. Therefore, the output will be an n-dimensional vector for a classifier with n classes and the sum of the elements in a vector is 1, as shown by Equation (6) [20,35]:…”

Section: (4) Classificationmentioning

confidence: 99%

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

Zhou

Zhu

et al. 2018

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Abstract:With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

show abstract

“…Third, processing techniques for vibration signals are abundant [ 7 ]. Therefore, the vibration-based method provides a potential approach to diagnose rope tension faults in hoisting systems [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Xue

Tan

Shi

et al. 2020

Entropy

View full text Add to dashboard Cite

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

show abstract

Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Cited by 10 publications

References 31 publications

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Contact Info

Product

Resources

About