Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10).

Download Full-text

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Measurement Science Review ◽

10.2478/msr-2021-0012 ◽

2021 ◽

Vol 21 (3) ◽

pp. 82-92

Author(s):

Mochao Pei ◽

Hongru Li ◽

He Yu

Keyword(s):

Feature Extraction ◽

Dynamic Analysis ◽

Vibration Signal ◽

Support Vector ◽

Nsga Ii ◽

Multi Scale ◽

State Identification ◽

Frequency Changes ◽

Degradation State Identification ◽

Degradation State

Abstract Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Download Full-text

Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Download Full-text

Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna

Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) ◽

10.20961/prosidingsnfa.v5i0.46615 ◽

2020 ◽

Vol 5 ◽

Author(s):

Toni Dwi Novianto ◽

I Made Susi Erawan

Keyword(s):

Image Processing ◽

Feature Extraction ◽

Region Of Interest ◽

Nearest Neighbors ◽

Support Vector ◽

Gray Level ◽

K Nearest Neighbors ◽

Eye Color ◽

Occurrence Matrix ◽

Fish Eye

Abstract: Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).Abstrak: Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (region of interest), konversi citra RGB menjadi grayscale, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu gray-level co-occurrence matrix (GLCM). Teknik klasifikasi yang digunakan yaitu, artificial neural network (ANN), k-nearest neighbors (k-NN), dan support vector machine (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan k-nearest neighbors (k-NN).

Download Full-text

Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

Advances in Mechanical Engineering ◽

10.1177/1687814016683085 ◽

2016 ◽

Vol 8 (12) ◽

pp. 168781401668308 ◽

Cited By ~ 1

Author(s):

Shuangyuan Wang ◽

Yixiang Huang ◽

Liang Gong ◽

Lin Li ◽

Chengliang Liu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Sparse Coding ◽

Extraction Procedure ◽

Vibration Signal ◽

Support Vector ◽

Data Set ◽

Vibration Signals ◽

Efficiency And Effectiveness ◽

Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

Download Full-text

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

Knowledge Engineering and Data Science ◽

10.17977/um018v1i22018p46-54 ◽

2018 ◽

Vol 1 (2) ◽

pp. 46

Author(s):

Tri Septianto ◽

Endang Setyati ◽

Joan Santoso

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Feature Extraction ◽

Template Matching ◽

Support Vector ◽

Gray Level ◽

Class 1 ◽

Occurrence Matrix ◽

Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

Download Full-text

Leaf Image Recognition Based on Bag of Features

Applied Sciences ◽

10.3390/app10155177 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5177 ◽

Cited By ~ 1

Author(s):

Yaonan Zhang ◽

Jing Cui ◽

Zhaobin Wang ◽

Jianfang Kang ◽

Yufang Min

Keyword(s):

Image Processing ◽

Feature Extraction ◽

Human Life ◽

Texture Features ◽

Output Pulse ◽

Support Vector ◽

Linear Discriminant ◽

Bag Of Features ◽

Good Ability ◽

Leaf Image

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

Download Full-text

Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

Sensors ◽

10.3390/s19081949 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1949 ◽

Cited By ~ 1

Author(s):

Yang Yuan ◽

Suliang Ma ◽

Jianwen Wu ◽

Bowen Jia ◽

Weixin Li ◽

...

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Support Vector ◽

Mechanical State ◽

Vibration Signals ◽

Feature Extraction Method ◽

Detection Technology ◽

Intelligent Detection ◽

Diagnosis Method ◽

Frequency Feature

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

Download Full-text

Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-07-2021-0167 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Swetha Parvatha Reddy Chandrasekhara ◽

Mohan G. Kabadi ◽

Srivinay Srivinay

Keyword(s):

Prostate Cancer ◽

Image Processing ◽

Feature Extraction ◽

Sensitivity And Specificity ◽

Early Stage ◽

Classification Model ◽

Support Vector ◽

Data Set ◽

Content Type ◽

Multiclass Svm

Purpose This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life. Design/methodology/approach The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer. Findings The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set. Originality/value The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

Download Full-text

Classification of heart disease using mfo based neural network on mri images

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210126153920 ◽

2021 ◽

Vol 17 ◽

Author(s):

Kalaivani. K ◽

Uma Maheswari. N ◽

Venkatesh. R

Keyword(s):

Neural Network ◽

Image Processing ◽

Feature Extraction ◽

Heart Disease ◽

Optimization Process ◽

Generalized Regression Neural Network ◽

Gray Scale ◽

Generalized Regression ◽

Scale Properties ◽

Optimal Feature

Background: Cardiovascular disease (CVD) is one of the primary diseases that cause death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, for every 34 seconds the people were died due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. Aims: The main aim of this work is to improve the performance of heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. Objective: The objective of this investigation is to diagnosis heart disease using feature extraction and reduction based classification using image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system in co-operates three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. Method: The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image is calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image is given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image and determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with regression nature. Due to the regression property the network is well trained with the feature. The Generalized regression neural network is used for classifying the heart disease. Results: The proposed method achieves the accuracy of 96.23%, sensitivity 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. Conclusion: In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods either the feature extraction based classification or the feature reduction based classification.

Download Full-text

A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique

International Journal of Rotating Machinery ◽

10.1155/2017/2607254 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Baiyan Chen ◽

Hongru Li ◽

He Yu ◽

Yukui Wang

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Vibration Signal ◽

Support Vector ◽

Characteristic Matrix ◽

Evaluation Technique ◽

Feature Extraction Method ◽

Hybrid Domain ◽

Degradation State ◽

Motor Bearing

The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.

Download Full-text