Application of Pattern Recognition to Fault Diagnosis

2017 ◽  
pp. 185-198
Author(s):  
Masoud Hajiaghajani
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis

Pattern Recognition Approach to Fault Diagnosis in the DAMADICS Benchmark Flow Control Valve

2003 ◽  
Vol 36 (5) ◽  
pp. 861-866 ◽  
Author(s):  
A. Marciniak ◽  
C.D. Bocăială ◽  
R. Louro ◽  
J. Sa da Costa ◽  
J. Korbicz
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Flow Control ◽  
Control Valve ◽  
Flow Control Valve ◽  
Pattern Recognition Approach

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

scholarly journals Research on rolling bearing fault diagnosis based on multi-dimensional feature extraction and evidence fusion theory

2019 ◽  
Vol 6 (2) ◽  
pp. 181488 ◽  
Author(s):  
Jingchao Li ◽  
Yulong Ying ◽  
Yuan Ren ◽  
Siyu Xu ◽  
Dongyuan Bi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Real Time ◽  
Rolling Bearing ◽  
Diagnostic Methods ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Fusion Theory

Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, a multi-dimensional feature extraction strategy on the basis of entropy characteristics, Holder coefficient characteristics and improved generalized box-counting dimension characteristics is executed for extracting health status feature vectors from vibration signals. And, secondly, a grey relation algorithm is used to calculate the basic belief assignments (BBAs) using the extracted feature vectors, and lastly, the BBAs are fused through the Yager algorithm for achieving bearing fault pattern recognition. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities in comparison with the existing intelligent diagnostic methods based on a small number of training samples with good real-time performance, and may be used for online assessment.

scholarly journals Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 238
Author(s):  
Zhuofei Xu ◽  
Yuxia Shi ◽  
Qinghai Zhao ◽  
Wei Li ◽  
Kai Liu
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Extreme Values ◽  
Rolling Bearing ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Printing Press ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Self Adaptive

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

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