Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

Mao, Wentao; He, Ling; Yan, Ye; Wang, Jinwan

doi:10.1016/j.ymssp.2016.06.024

Cited by 190 publications

(64 citation statements)

References 26 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the traditional NN architecture, deep ConvNet combined the skipping layer with the last convolutional layer, as input to the multiscale layer, to simultaneously maintain global and local information. Mao et al [91] later presented a new OS-ELM approach for solving the online sequential data imbalance issue. The acquired granules and principal curves were rebuilt online using the bearing data which arrived in sequence, and after an over-and under-sampling process, the balanced sample set was used to update the diagnosis model dynamically.…”

Section: Bearingsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

show abstract

Section: Bearingsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

show abstract

“…At the same time, the parameters of SVM and F-SVM have also been optimized using PSO. In the canonical PSO, each particle i has position z i and velocity v i that is updated at each iteration according to equation (19) …”

Section: Construct Classification Modelmentioning

confidence: 99%

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Wang

2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

show abstract

“…An online sequential prediction method for imbalanced fault diagnosis problem based on extreme learning machine is proposed in [16], where under-sampling and over-sampling techniques plays again an important role. Using two typical bearing fault diagnosis data, results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

et al. 2017

View full text Add to dashboard Cite

This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy.

show abstract

Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

Cited by 190 publications

References 26 publications

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

Contact Info

Product

Resources

About