Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis

Ahmad, Zahoor; Rai, Akhand; Maliuk, Andrei S.; Kim, Jong‐Myon

doi:10.1109/access.2020.3022770

Cited by 37 publications

(20 citation statements)

References 44 publications

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“…For dataset 1 (pressure 3.0 bar), it can be observed that the CWT coefficients possess large magnitudes at lower frequency scales and the energy of vibration signal is mainly concentrated around the lower frequency regions, when the vibration signal is in healthy condition. For the MSH and MSS types fault, the in-plane torsional vibrations and out-plane lateral vibrations produce additional frequency modes into the vibration signals [57]. Thus, the energy of the vibration signal spreads across the different frequency scales in the CWTS plots, ranging from low to high.…”

Section: Experimental Verification -Results and Discussionmentioning

confidence: 99%

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

et al. 2021

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Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and generally, statistical features are extracted from the vibration signals to retain meaningful fault information. However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults, thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT) is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault information in both the time and frequency domains. The CWT scales form two-dimensional timefrequency images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images (SGI). Over the past few decades, CNN models have been established as an effective practice to process images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed existing methods with an average performance improvement of 4.7 -15.6%.

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Section: Experimental Verification -Results and Discussionmentioning

confidence: 99%

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

et al. 2021

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“…The SVM algorithm works based on the concept of statistical learning theory, and in multi-class classification problems, an SVM is one of the best performing machine learning algorithms. Many researchers have utilized and suggested SVMs for distinct real-life applications such as a system fault diagnosis or abnormality detection and the monitoring of patients [ [160] , [161] , [162] , [163] ]. The SVM algorithm generates a hyperplane or linear line as a decision boundary to separate different types of data points for classification tasks.…”

Section: Machine Learning For Detection Of Covid-19 Symptomsmentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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“…In the past, ML-based approaches have been effectively used on a variety of classification problems [52][53][54][55][56]. We used a deep learning-based scheme called ResNet in this work to identify different human activities and detect falling using the generated spectrograms.…”

Section: Residual Neural Network (Resnet) For Classificationmentioning

confidence: 99%

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Saeed

Shah

et al. 2021

Electronics

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Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

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Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis

Cited by 37 publications

References 44 publications

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

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