Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern

Nam, Jaehyeon; Kang, Jaeyoung

doi:10.3390/s21238054

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…12C). Either the CNN is trained to classify different RPs [129][130][131][132], or to predict time series values [115]. Such combinations of RPs and RQA measures with machine learning were successfully applied for transition detection, monitoring, and anomaly detection [80,[133][134][135][136].…”

Section: Recurrence and Machine Learningmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.

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Section: Recurrence and Machine Learningmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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“…From a machine learning perspective, each set of measurement vectors is a labeled sample. Based on previous studies [56][57][58] and our own experience, it was determined that the number of samples was not sufficient to prepare a machine learning model. This situation is very common in real industrial systems in which the amount of data is limited.…”

Section: Recorded Data Preprocessingmentioning

confidence: 99%

“…The samples for the testing set were selected at random; however, the proportions of individual categories (reliability states) were maintained as they existed in the total set. To check whether the selection maintained the correct category distribution, the Based on previous studies [56][57][58] and our own experience, it was determined that the number of samples was not sufficient to prepare a machine learning model. This situation is very common in real industrial systems in which the amount of data is limited.…”

Section: Recorded Data Preprocessingmentioning

confidence: 99%

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Pająk,

Kluczyk,

Muślewski

et al. 2023

Electronics

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One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

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“…Research efforts have demonstrated that CNNs are effective in studies and applications involving chaotic signals, such as chaotic biomedical signal analysis, speech processing, and chaos identification systems [48][49][50]. Moreover, the selection of CNNs allows us to gain useful theoretical insight into the proposed system.…”

Section: Cnn-based Receivermentioning

confidence: 99%

Design of an M-Ary DLCSK Communication System Using Deep Transfer Learning

Mobini,

Herceg,

Kaddoum

2023

IEEE Open J. Commun. Soc.

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Conventional coherent chaos-based communication systems require synchronization of chaotic signals, which is still practically unattainable in a noisy environment. Moreover, in non-coherent schemes, a part of the bit duration is spent sending non-information-bearing reference samples, which deteriorates the Bit Error Rate performance (BER) of these systems. To tackle these problems, this paper designs an M -ary Deep Learning Chaos Shift Keying (M -ary DLCSK) system. The proposed receiver uses a Convolutional Neural Network (CNN)-based classifier that recovers M -ary modulated data. The trained NN model grasps different chaotic maps, estimates channels, and classifies the received signals effectively. Moreover, we consider a Transfer Learning (TL) framework that enhances the noise performance and classification results. Due to the generalization capabilities of TL, the trained NN can work in different Signal-to-Noise Ratio (SNR) conditions without the need for re-training. We compare the BER performance, complexity, and bandwidth efficiency of the M -ary DLCSK receiver with existing receivers. The results demonstrate that the M -ary DLCSK receiver is the first practical system that achieves the theoretical BER performance of the coherent CSK systems under Rayleigh fading channels. Moreover, the proposed system provides a considerable performance advantage compared to the existing DL-based receivers under Rayleigh fading channels. For example, the BER performance of 8-ary DLCSK shows a gain of 0.1 over the Long Short-Term Memory (LSTM)-aided DNN systems at the target E b /N 0 = 14dB. These features make M -ary DLCSK an attractive candidate for several applications, such as Massive Multiple-Input Multiple Output (MIMO), Vehicle-to-everything (V2X), Quantum, and optical communication systems.

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Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern

Cited by 10 publications

References 38 publications

Trends in recurrence analysis of dynamical systems

Trends in recurrence analysis of dynamical systems

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Design of an M-Ary DLCSK Communication System Using Deep Transfer Learning

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