Track Occupation Detection Based on a Maximum Posterior Probability Model Using Multisensor Data Fusion

Zhong, Hua; Sun, Ziwei; Jia, Huiwen; Zhao, Zeyu; Hou, Lei

doi:10.1109/mcse.2018.2882330

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Then, each normalized sample d i {d i1 , d i2 , ., d in } is averaging for all data. Finally, the MD is calculated to evaluate the external quality of d i , and it is expressed in equation (7).…”

Section: Data-level Fusionmentioning

confidence: 99%

“…6 For crack detection, many sensors are widely utilized to provide useful information, such as sound, vibration, acoustic emission (AE), etc. 7 However, for complex working conditions with strong background noise, the single signal has insufficient capacity in weak crack detection. 8 As a result, it is urgent to make up for the limitations by fusing abundant information from multi sensors in data, feature, and decision level.…”

Section: Introductionmentioning

confidence: 99%

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Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.

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Section: Data-level Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…This would support the diagnosis process and the maintenance decision-making [16,17]. On the other hand, the result of the diagnosis from the analysis of CIs can be improved by having several sensors to monitor the machinery because it would allow performing Data -Fusion [18,19]. The extraction of indicators in time and frequency domains requires a lower computational cost than the required one for calculating indicators in the time-frequency domain [20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles

et al. 2020

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Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.

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A property perceived service quality evaluation method for public buildings based on multisource heterogeneous information fusion

Zuo

Liu

et al. 2023

Engineering Applications of Artificial Intelligence

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Track Occupation Detection Based on a Maximum Posterior Probability Model Using Multisensor Data Fusion

Cited by 3 publications

References 5 publications

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles

A property perceived service quality evaluation method for public buildings based on multisource heterogeneous information fusion

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