[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Elsamanty et al., 2021 - Google Patents

Rotary Machines Fault Diagnosis based on Principal Component Analysis

Elsamanty et al., 2021

View PDF
Document ID
11534184725666335581
Author
Elsamanty M
S Salman W
A Ibrahim A
Publication year
Publication venue
Engineering Research Journal

External Links

Snippet

Rotating machines are commonly used in industrial applications. Mechanical faults such as rotor unbalance, shaft misalignment, pulley misalignment, structural looseness, and bearing faults leading to unplanned shutdown based on the severity of these faults. The condition …
Continue reading at journals.ekb.eg (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Testing of bearings
    • G01M13/045—Testing of bearings by acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Testing of gearing or of transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]

Similar Documents

Publication Publication Date Title
Huo et al. Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures
Song et al. Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory
Pandarakone et al. Deep neural network based bearing fault diagnosis of induction motor using fast Fourier transform analysis
Soylemezoglu et al. Mahalanobis-Taguchi system as a multi-sensor based decision making prognostics tool for centrifugal pump failures
Farokhzad Vibration based fault detection of centrifugal pump by fast fourier transform and adaptive neuro-fuzzy inference system
Goyal et al. Applications of digital signal processing in monitoring machining processes and rotary components: a review
CN111365251B (en) Intelligent diagnosis method for faults of centrifugal pump unit
Khoualdia et al. Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals
Prosvirin et al. An improved algorithm for selecting IMF components in ensemble empirical mode decomposition for domain of rub-impact fault diagnosis
Lu et al. CEEMD-assisted bearing degradation assessment using tight clustering
Elsamanty et al. Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data
Plante et al. Rotating machine fault detection using principal component analysis of vibration signal
Paudyal et al. Local maximum acceleration based rotating machinery fault classification using KNN
Chennippan et al. Vibration Signals Based Bearing Defects Identification Through Online Monitoring Using LABVIEW.
Elsamanty et al. Rotary Machines Fault Diagnosis based on Principal Component Analysis
Wang et al. Multiscale sparsity measure fusion for bearing performance degradation assessment
Srinivas et al. Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow
Jawad et al. A data-driven approach based bearing faults detection and diagnosis: A review
Nejadpak et al. Misalignment and unbalance faults detection and identification using KNN analysis
Madhukar et al. A Machine Learning Based Methodology for Fault Diagnosis in Rotating Machine
Mustafa et al. Experimental research on machinery fault simulator (MFS): A review
Samuel et al. Survey of Gear Fault Diagnosis Using Various Statistical Signals Parameters
Saha et al. Different Machine Maintenance Techniques of Rotary Machine and Their Future Scopes: A Review
Lim et al. Motor fault detection method for vibration signal using FFT residuals
Nayak et al. design and development of machine fault simulator (MFS) for fault diagnosis