Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring

Liu, Haining; Wang, Yixiang; Li, Fajia; Wang, Xiaohong; Liu, Chengliang; Pecht, Michael

doi:10.1109/access.2019.2940381

Cited by 11 publications

(8 citation statements)

References 43 publications

(43 reference statements)

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“…Due to the data characteristics of one‐dimensional data, feature extraction methods using the transform domain predominate. Liu et al 33 used wavelet packet transform (WPT) and two‐dimensional discrete cosine transform (2D‐DCT) in the process of feature extraction for one‐dimensional vibration data. Wang et al 34 used discrete wavelet transform (DWT) for hash feature extraction and finally for covert channel detection.…”

Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

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Network covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one‐dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one‐dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

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Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Pham_2020 [131] Ambika_2019 [132] Nissila_2019 [133] Tong_2018 [134] Jayakumar_2017 [135] Huo_2017 [136] Li_2016c [137] Hua_2015 [138] Gelman_2015 [139] Gelman_2014 [140] Tse_2013a [141] Li_2013a [142] Luo_2003 [143] He_2017 [144] Kawada_2003 [145] Gelman_2020 [146] Hartono_2019 [147] Puchalski_2019 [148] Gelman_2017a [149] Gelman_2017b [150] Stander_2002 [151] Shu_2020 [152] Liu_2019b [153] Jayakumar_2017 [135] Antoni_2002 [154] Xiao_2020 [11] Liu_2019a [155] You_2019 [156] Antoni_2006 [157] Signal decomposition (EMD, EEMD, LMD, SVD, VMD)…”

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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“…This approach enables the differentiation of data categories and enhances transmission, storage, and computational efficiency due to the compact nature of hash coding. Liu et al [5] introduced perceptual hashing as a means for machine condition information extraction, mapping vibration signal data into brief mechanical condition hash codes, and achieving high diagnostic accuracy with reduced data storage and computational demands. However, extracting perceptual hash features through signal processing requires extensive expertise, and employing machine learning for this purpose is more advantageous [6].…”

Section: Introductionmentioning

confidence: 99%

Incremental perceptual vibration hashing autoencoder for machine condition monitoring

li,

liu

et al. 2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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Feature extraction is of significant importance for machine condition monitoring, which extracts the potential machine condition information from monitored data. Autoencoder can extract the inherent structural feature in data, thus widely used for feature extraction from vibration signals. However, the training of autoencoder is a time-consuming process. Especially when facing a practical condition monitoring application in which numerous abnormal conditions emerge successively, an autoencoder needs to be retrained to adapt to the new faulty data, thus the training time multiplies. This paper develops the incremental perceptual vibration hashing autoencoder (IPVHAE), dedicated to mapping the inherent machine condition information in vibration signals into a compact, low-dimensional machine condition hash (MCH). An incremental learning approach based on parameter constraints is employed to facilitate the retraining of the IPVHAE on new data. The parameters of an autoencoder only need to be updated with the new data instead of all the data under available machine conditions. The effectiveness of the developed algorithm is verified using the Case Western Reserve University bearing dataset. Results show that the machine condition hash generated by the IPVHAE can balance low dimensionality with high discriminability, meanwhile minimizing the time and computing cost during the incremental learning process.

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Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring

Cited by 11 publications

References 43 publications

A generalized detection framework for covert timing channels based on perceptual hashing

A generalized detection framework for covert timing channels based on perceptual hashing

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Incremental perceptual vibration hashing autoencoder for machine condition monitoring

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