Intelligent bearing faults diagnosis featuring Automated Relative Energy based Empirical Mode Decomposition and novel Cepstral Autoregressive features

Aziz, Sumair; Khan, Muhammad Umar; Faraz, Muhammad; Montes, Gabriel Axel

doi:10.1016/j.measurement.2023.112871

Cited by 12 publications

(2 citation statements)

References 47 publications

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“…Then a threshold is set and all the IMFs above or below that relative energy threshold are added together to construct the pre-processed signal (Aziz et al, 2023). In our case, we have set the threshold at 3.5% and all the IMFs having relative energies above this threshold were added together to form the preprocessed signal (>3.5%).…”

Section: Methodsmentioning

confidence: 99%

Machine learning‐based classification of multiple heart disorders from PCG signals

et al. 2023

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Timely and accurate detection and diagnosis of heart disorders is a significant problem in the medical community since the mortality rate is increasing. Pulsing of cardiac structures and blood turbulence creates heart sounds recorded and detected through Phonococardiogram (PCG). As a non‐invasive technique, PCG signals have a strong ability to be used for designing automatic classification of possible heart disorders. This paper presents an expert system design for the detection and classification of PCG signals for five classes, namely, healthy, aortic stenosis, mitral stenosis, mitral regurgitation, and mitral valve prolapse. In this work, a single‐channel PCG signal is first decomposed using Empirical Mode Decomposition (EMD) into different modes known as intrinsic mode functions (IMFs). Manual signal analysis is applied to identify the relevant IMFs to construct a preprocessed signal. We proposed an automated energy‐based signal reconstruction through IMFs. The proposed algorithms automatically identify the relevant IMFs and added them together to form a preprocessed signal. After preprocessing, the first nine features of Mel Frequency Cepstral Coefficients (MFCC) were computed and passed to several classification methods such as Fine Tree, Quadratic Discriminant, Kernel Naive Bayes, Support Vector Machines (SVM), Fine K‐Nearest Neighbours (Fine‐KNN), Ensemble Bagged Trees and Neural Network. The best performance of 99.3% accuracy was obtained via Fine‐KNN using 10‐fold cross‐validation. The proposed method was evaluated on a publicly available dataset of heart sounds. The proposed method demonstrated improved performance as compared to the existing state‐of‐the‐art methods.

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Section: Methodsmentioning

confidence: 99%

Machine learning‐based classification of multiple heart disorders from PCG signals

et al. 2023

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“…The method of diagnosing abnormal equipment states based on the temperature changes of components is widely used in the fault diagnosis of wind turbines [1]. Vibration signals, lubricating oil analysis, and acoustic emission are used to detect faults in rolling bearings, large pumping station units, and motors [2][3][4][5]. However, with the rise of equipment manufacturing processes and the increase in their complexity, such methods based on the principles of the equipment itself not only need to comprehensively consider the influence of multiple relevant factors, but they also put forward high requirements for experts and their experience and analytical ability in fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

Chen,

Zhao,

Shen

et al. 2024

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To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.

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