Improved Structural Rotor Fault Diagnosis using Multi-sensor Fuzzy Recurrence Plots and Classifier Fusion

2021 ◽  
pp. 1-1
Author(s):  
Aneesh G Nath ◽  
Sandeep S. Udmale ◽  
Divyanshu Raghuwanshi ◽  
Sanjay Kumar Singh
Keyword(s):  
Fault Diagnosis ◽  
Classifier Fusion ◽  
Recurrence Plots

scholarly journals Fault Diagnosis of Nonlinear Analog Circuit Based on Generalized Frequency Response Function and LSSVM Classifier Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jialiang Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Response ◽  
Frequency Response Function ◽  
Analog Circuit ◽  
Least Square ◽  
Classifier Fusion ◽  
Support Vector ◽  
Response Functions ◽  
Nonlinear Analog

For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

A classifier fusion system for bearing fault diagnosis

2013 ◽  
Vol 40 (17) ◽  
pp. 6788-6797 ◽  
Author(s):  
Luana Batista ◽  
Bechir Badri ◽  
Robert Sabourin ◽  
Marc Thomas
Keyword(s):  
Fault Diagnosis ◽  
Classifier Fusion ◽  
Fusion System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

2009 ◽  
Vol 58 (3) ◽  
pp. 602-611 ◽  
Author(s):  
Kihoon Choi ◽  
S. Singh ◽  
A. Kodali ◽  
K.R. Pattipati ◽  
J.W. Sheppard ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classifier Fusion ◽  
Automotive Systems

scholarly journals Novel classifier fusion approahces for fault diagnosis in automotive systems

2007 ◽  
Author(s):  
Kihoon Choi ◽  
Satnam Singh ◽  
Anuradha Kodali ◽  
Krishna R. Pattipati ◽  
John W. Sheppard ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classifier Fusion ◽  
Automotive Systems

Data-Driven Fault Diagnosis of Chemical Processes Based on Recurrence Plots

2021 ◽  
Vol 60 (7) ◽  
pp. 3038-3055
Author(s):  
Hooman Ziaei-Halimejani ◽  
Reza Zarghami ◽  
Seyed Soheil Mansouri ◽  
Navid Mostoufi
Keyword(s):  
Fault Diagnosis ◽  
Chemical Processes ◽  
Data Driven ◽  
Recurrence Plots

scholarly journals Fault diagnosis in railway track circuits using Dempster–Shafer classifier fusion

2010 ◽  
Vol 23 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Latifa Oukhellou ◽  
Alexandra Debiolles ◽  
Thierry Denœux ◽  
Patrice Aknin
Keyword(s):  
Fault Diagnosis ◽  
Railway Track ◽  
Classifier Fusion ◽  
Track Circuits

scholarly journals Multi-Channel IoT-Based Ensemble-Features Fault Diagnosis for Machine Condition Monitoring

2020 ◽  
Author(s):  
Shang Gao ◽  
Cuicui Du
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Classifier Fusion ◽  
Machine Condition ◽  
Rolling Ball ◽  
Dempster Shafer Theory ◽  
Machine Condition Monitoring ◽  
Shafer Theory ◽  
Fusion Approach

This paper proposes a multi-channel internet of things (IoT)-based industrial wireless sensor network (IWSN) with ensemble-features fault diagnosis for machine condition monitoring and fault diagnosis. In this paper, the rolling bearing is taken as an example of monitored industrial equipment due to its wide use in industrial processes. The rolling bearing vibration signals are measured for further processing and analysis. On-sensor node ensemble feature extraction and fault diagnosis using Back Propagation network are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four rolling bearing operating in cage fracture, rolling ball spalling, inner ring spalling and outer ring spalling are monitored to evaluate the proposed system. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 94.21%, proving the feasibility of the proposed method to identify the bearing-fault patterns. This paper is conducted to provide new insights into how a high-accuracy IoT-based ensemble-features fault diagnosis algorithm is designed and further giving advisable reference to more IWSNs scenarios.

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