Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis with Neuromorphic Computing

Chen, Xinrui; Li, Xiang; Yu, Shupeng; Lei, Yaguo; Li, Naipeng; Yang, Bin

doi:10.1109/jas.2023.124107

Cited by 24 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ratio ( 13) makes it possible to control the risk of the estimate Â(t) based on noise dispersion σ 2 k,n . Moreover, having estimated the risk for different bases from the dictionary Ω and using (13), we can determine the wavelet basis giving the least losses.…”

Section: Optimization Of the Hmts Determination Of The Wavelet Basis ...mentioning

confidence: 99%

“…A new approach was suggested in [12] to forecast the operation capability of a dry gas transmission system and to classify whether the amount of metal loss was effective even in the case of the absence of a priori data. For the problem of the diagnostics of rotating machine faults, a group of researchers [13] developed a model of a neural network with deep momentum transfer (SNN). The investigations in [13] showed that the developed method allows one to carry out contactless processing of visual data event fluxes, distinguish features and make diagnostics of machine faults.…”

Section: Introductionmentioning

confidence: 99%

“…For the problem of the diagnostics of rotating machine faults, a group of researchers [13] developed a model of a neural network with deep momentum transfer (SNN). The investigations in [13] showed that the developed method allows one to carry out contactless processing of visual data event fluxes, distinguish features and make diagnostics of machine faults.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

Mandrikova,

Mandrikova

2024

Mathematics

View full text Add to dashboard Cite

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure.

show abstract

Section: Optimization Of the Hmts Determination Of The Wavelet Basis ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

Mandrikova,

Mandrikova

2024

Mathematics

View full text Add to dashboard Cite

show abstract

Remaining Useful Life Prediction of Rolling Bearings Based on Adaptive Continuous Deep Belief Networks and Improved Kernel Extreme Learning Machine

Zhou,

Wang,

Shi

et al. 2024

Adaptive Control & Signal

View full text Add to dashboard Cite

Rolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real‐time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA‐KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature.

show abstract

CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

View full text Add to dashboard Cite

In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

show abstract

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis with Neuromorphic Computing

Cited by 24 publications

References 17 publications

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

Remaining Useful Life Prediction of Rolling Bearings Based on Adaptive Continuous Deep Belief Networks and Improved Kernel Extreme Learning Machine

CVApool: using null-space of CNN weights for the tooth disease classification

Contact Info

Product

Resources

About