Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare

Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare

Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission

The gear system is a critical component in the machinery and predicting the performance of a gear system is an important function. Unpredictable failures of a gear system can cause serious threats to human life, and have large scale economic effects. It is necessary to inspect gear teeth periodically to identify crack propagation and, other damages at the earliest. This study has two main objectives. Firstly, the research predicted and classified specific film thickness (λ) of spur gear by Artificial Neural Network (ANN) and Regression models. Parameters such as acoustic emission (AE), temperature and specific film thickness (λ) data were extracted from works of other researchers. The acoustic emission signals and temperature were used as input to ANN and Regression models, while (λ) was the output of the models. Second objective is to use the third generation ANN (Spiking Neural Network) for fault diagnosis and classification of spur gear based on AE signal. For this purpose, a test rig was built with several gear faults. The AE signal was processed through preprocessing, features extraction and selection methods before the developed ANN diagnosis and classification model were built. These processes were meant to improve the accuracy of diagnosis system based on information or features fed into the model. This research investigated the possibility of improving accuracy of spur gear condition monitoring and fault diagnoses by using Feed-Forward Back- Propagation Neural Networks (FFBP), Elman Network (EN), Regression Model and Spiking Neural Network (SNN). The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. For gear fault diagnosis, the SNN achieved nearly 97% accuracy in its diagnosis. Finally, the methods use in the study have proven to have high accuracy and can be used as tools for prediction, classification and fault diagnosis in spur gear

Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform

This paper is about diagnosis and classification of bearing faults using Neural Networks (NN), employing nondestructive tests. Vibration signals are acquired by a bearing test machine. The acquired signals are preprocessed using discrete wavelet analysis. Standard deviation of discrete wavelet coefficient is chosen as the distinguishing feature of the faults. This feature vector is given to the design network as inputs. The input vector is normalized prior to be applied to neural network. There are four output neurons each of which corresponds to: 1) bearing with inner race fault, 2) bearing with outer race fault, 3) bearing with ball defect, and 4) normal bearing. The structure of NN is 6:20:4 and with 99 % performance

The Research on Sensor Optimal Layout on Detection Device for Automobile Seat Horizontal Drive Mechanism

The type of the sensor and the optimal placement of the installation location of the sensor are important factors in vibration signal acquisition on automobile seat horizontal drive mechanism (HDM) for quality grading. According to the characteristics of HDM, the actual installation condition and the attenuation characteristic of the vibration signal transmission, the type of sensor was selected reasonably. By means of finite element analysis method, the best placement of the sensors to mount on the fixture was analyzed aim to maximize the information reflecting the fault and be sensitivity to the direction of the vibration. The finite element simulation and experimental results show that the acceleration sensor can effectively detect the fault characteristic signal, which provides a basis for further quality grading for HDM

Fault diagnosis of gears based on local mean decomposition combing with kurtosis

Local Mean Decomposition (LMD) is a new self-adaptive time frequency analysis method. In present paper, the effectiveness of LMD method to extract fault features of gears, which are multi-component amplitude modulation (AM) and frequency modulation (FM), is demonstrated. A series of tests on tooth wearing, breaking and spalling gears are conducted and analyzed by LMD. And the fault features extracted by LMD are compared with those obtained from conventional Hilbert transform (HT). Moreover, the gear faults are identified by kurtosis based on LMD decomposed signals. The results demonstrate that the scheme combining LMD method with kurtosis analysis is effective to extract the characteristics of fault gears and improve the accuracy of fault diagnosis of gears

Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform

This paper is about diagnosis and classification of bearing faults using Neural Networks (NN), employing nondestructive tests. Vibration signals are acquired by a bearing test machine. The acquired signals are preprocessed using discrete wavelet analysis. Standard deviation of discrete wavelet coefficient is chosen as the distinguishing feature of the faults. This feature vector is given to the design network as inputs. The input vector is normalized prior to be applied to neural network. There are four output neurons each of which corresponds to: 1) bearing with inner race fault, 2) bearing with outer race fault, 3) bearing with ball defect, and 4) normal bearing. The structure of NN is 6:20:4 and with 99 % performance

Blade fault diagnosis using artificial intelligence technique

Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis