[go: up one dir, main page]

Mutra et al., 2023 - Google Patents

Signal-based parameter and fault identification in roller bearings using adaptive neuro-fuzzy inference systems

Mutra et al., 2023

View PDF
Document ID
11694842166877762143
Author
Mutra R
Mallikarjuna Reddy D
Srinivas J
Sachin D
Babu Rao K
Publication year
Publication venue
Journal of the Brazilian Society of Mechanical Sciences and Engineering

External Links

Snippet

The rolling element bearings are used in high load-bearing, high stiffness, and high-speed applications. They have wide applications in aero-engine and automobile rotors. In practice, major rotor failures occur with bearing faults. Therefore, it is required to identify the location …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Eren et al. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Souza et al. Deep learning for diagnosis and classification of faults in industrial rotating machinery
Helmi et al. Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS
Youcef Khodja et al. Rolling element bearing fault diagnosis for rotating machinery using vibration spectrum imaging and convolutional neural networks
Ertunc et al. ANN-and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults
Lee et al. Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery
Jayaswal et al. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis
Lobato et al. An integrated approach to rotating machinery fault diagnosis using, EEMD, SVM, and augmented data
Gültekin et al. A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images
Mutra et al. Signal-based parameter and fault identification in roller bearings using adaptive neuro-fuzzy inference systems
de Sá Só Martins et al. Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines
Gougam et al. Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system
Islam et al. Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings
Omoregbee et al. Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model
Sharma et al. Fault diagnosis of rolling element bearings using fractional linear prediction and AI techniques
Rakesh et al. Application of EMD ANN and DNN for self-aligning bearing fault diagnosis
Lu et al. CEEMD-assisted bearing degradation assessment using tight clustering
Attoui Novel fast and automatic condition monitoring strategy based on small amount of labeled data
Ümütlü et al. Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN
Nashed et al. Gas turbine failure classification using acoustic emissions with wavelet analysis and deep learning
Benmahdi et al. RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults
Sharma et al. Life assessment and health monitoring of rolling element bearings: an experimental study
Bengherbia et al. Design and hardware implementation of an intelligent industrial iot edge device for bearing monitoring and fault diagnosis
Merkelbach et al. Using vibration data to classify conditions in disk stack separators
Yadav et al. Variable dropout one-dimensional CNN for vibration-based shaft unbalance detection in industrial machinery