Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Genetic Algorithms ◽  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Vibration Data ◽  
Neuro Fuzzy ◽  
Machine Fault ◽  
The Time Domain ◽  
Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

A review of stochastic resonance in rotating machine fault detection

2019 ◽  
Vol 116 ◽  
pp. 230-260 ◽  
Author(s):  
Siliang Lu ◽  
Qingbo He ◽  
Jun Wang
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Rotating Machine ◽  
Machine Fault

scholarly journals Using Artificial Intelligence to Determine the Type of Rotary Machine Fault

MENDEL ◽  
2018 ◽  
Vol 24 (2) ◽  
Author(s):  
Daniel Zuth ◽  
Tomas Marada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Domain ◽  
Measured Data ◽  
Rotary Machine ◽  
Rotating Machine ◽  
Data Source ◽  
Machine Fault ◽  
The Time Domain ◽  
Types Of Faults

The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classication learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.

Rotating machine fault detection using principal component analysis of vibration signal

2016 ◽  
Author(s):  
Tristan Plante ◽  
Lucas Stanley ◽  
Ashkan Nejadpak ◽  
Cai Xia Yang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Rotating Machine ◽  
Machine Fault

Fault Detection and Diagnostics in an Ocean Turbine Using Vibration Analysis

2010 ◽  
Author(s):  
Mustapha Mjit ◽  
Pierre-Philippe J. Beaujean ◽  
David J. Vendittis
Keyword(s):  
Fault Detection ◽  
Diagnostic Techniques ◽  
Fault Detection And Diagnosis ◽  
Vibration Data ◽  
Wide Range ◽  
Detection And Diagnosis ◽  
Early Fault Detection ◽  
Monitoring And Diagnostics ◽  
Machine Speed ◽  
Diagnostics System

This paper describes the approach, procedure and techniques developed to evaluate the health of ocean turbines, based on vibration measurements and analyses. A LabVIEW model for on-line vibration condition monitoring, implemented with advanced diagnostic techniques features, was developed. In order to distinguish between a vibration amplitude change due to a developing fault and that due to a change in operating condition, this program includes the use of an ordering technique in the frequency domain, which relates the vibration to the machine speed. Some experiments were first performed on a commercial fan to illustrate and demonstrate the fault detection capability of the monitoring and diagnostics system. To increase the reliability of the monitoring system, and to demonstrate that it can be used for monitoring a wide range of machines, a second series of vibration data collection and monitoring events was performed on a small boat with different combination (on/off status) of the engine, hydraulic pump, generator and air conditioning. This allowed for the detection of the frequency components associated with each subsystem, alone and together, and enabled the detection of mechanical faults, such as imbalance and misalignment, if they existed. For long term monitoring, the model allow for the automatic storing of raw data either periodically and/or after any deviations from normal conditions, i.e., when alerts are on. This makes it possible to follow the progress (towards an alarm condition) of any faults without saving data continuously. In this way, measurements of unexpected events may be made without the vibration engineer’s physical presence, hopefully, early fault detection and diagnosis will avoid catastrophic failure from occurring. This enables the economic and efficient health monitoring of ocean turbines as they become operational.

scholarly journals A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Zrar Kh. Abdul ◽  
Abdulbasit Al-Talabani ◽  
Ayub O. Abdulrahman
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Local Binary Pattern ◽  
Vibration Signal ◽  
Support Vector ◽  
Vibration Data ◽  
Rotating Machine ◽  
Research Areas ◽  
Gear Fault ◽  
New Feature

Gear fault detection is one of the underlying research areas in the field of condition monitoring of rotating machines. Many methods have been proposed as an approach. One of the major tasks to obtain the best fault detection is to examine what type of feature(s) should be taken out to clarify/improve the situation. In this paper, a new method is used to extract features from the vibration signal, called 1D local binary pattern (1D LBP). Vibration signals of a rotating machine with normal, break, and crack gears are processed for feature extraction. The extracted features from the original signals are utilized as inputs to a classifier based onk-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) for three classes (normal, break, or crack). The effectiveness of the proposed approach is evaluated for gear fault detection, on the vibration data obtained from the Prognostic Health Monitoring (PHM’09) Data Challenge. The experiment results show that the 1D LBP method can extract the effective and relevant features for detecting fault in the gear. Moreover, we have adopted the LOSO and LOLO cross-validation approaches to investigate the effects of speed and load in fault detection.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

scholarly journals A predictive equation for residual strength using a hybrid of Subset Selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming

2021 ◽  
pp. 101222
Author(s):  
Hossien Riahi-Madvar ◽  
Mahsa Gholami ◽  
Bahram Gharabaghi ◽  
Seyed Morteza Seyedian
Keyword(s):  
Genetic Programming ◽  
Residual Strength ◽  
Subset Selection ◽  
Pareto Optimal ◽  
Predictive Equation ◽  
Selection Of
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