Multiclass Relevance Vector Machine Research Articles

Small sample composite fault diagnosis of hydraulic bearings based on improved VME algorithm and mRVM

The working environment of rolling bearings is complex. Once a fault occurs, various parts will affect each other and produce a compound fault. Traditional methods often use signal separation algorithms to separate different types of signals for fault diagnosis, but it is difficult to analyze specific faults efficiently and accurately. To solve this problem, this paper combines variational mode decomposition (VMD), Laplace energy index (LE) and variational mode extraction (VME) for signal extraction. Multi-class relevance vector machine (mRVM) and DS evidence theory are used for intelligent fault diagnosis, focusing on the context of small sample data. First, the VMD-LE-VME method is used to extract effective fault information from the fault signal and obtain multi-domain features. Then, the multi-domain features are input into mRVM for fault identification. Finally, the classification results are fused through DS evidence theory to obtain the final classification results. The effectiveness and superiority of this method in processing small sample data are verified by experiments.

Rolling bearing fault diagnosis method based on SSA-mRVM

In order to accurately diagnose the faults of rolling bearings, in this paper, a fault diagnosis method is proposed based on multiclass relevance vector machines (mRVM) optimized by the sparrow search algorithm (SSA). Complete ensemble empirical mode decomposition (CEEMD) is applied to feature extraction of vibration signals. Optimizing the parameters of multiclass relevance vector machines involves the use of the sparrow search algorithm. The optimized multiclass relevance vector machines model is applied for fault diagnosis to obtain accurate diagnostic results.

Faults Diagnosis for Large-Scale Battery Packs via Texture Analysis on Spatial–Temporal Images Converted From Electrical Behaviors

Battery failures have become the most intractable obstacles undermining the market confidence in applications like electric vehicle and power grid energy storage. This paper aims to fashion a generic diagnosis scheme against the faults in large-scale battery systems. First, a voltmeter array-based anomaly perception mechanism against the electrical behaviors of battery packs is developed. Then, system information on spatial arrangement and temporal dynamics is organically fused and drawn as a kind of pseudo 2D images (P2Is). Afterwards, by analyzing the resultant P2Is with the 2D variational mode decomposition (2D-VMD) and gray level co-occurrence matrix (GLCM), some statistical quantities concerning multi-scale texture features, extracted and refined by the principal component analysis (PCA), are found to have strong indicative associations with battery fault type and fault grade. Finally, relying on the multi-class relevance vector machine (M-RVM), feature evidences are synthesized to detect fault occurrence and give judgements on fault specifics of type and severity. Experimental verifications on a li-ion battery pack with 180 cells suggest that the proposed scheme behaves well in fault type isolating, with an accuracy rate of 97.6% and in fault severity grading, with an accuracy rate of 84.67%.

Data-Driven Diagnosis of Multiple Faults in Series Battery Packs Based on Cross-Cell Voltage Correlation and Feature Principal Components

This article develops an efficient fault diagnostic scheme for battery packs using a novel sensor topology and signal processing procedure. Cross-cell voltages are measured to capture electrical abnormalities, and recursive correlation coefficients between adjacent voltages are calculated to embody system state. Then discrete wavelet packet transform is applied on the correlation sequences to extract diverse characteristic indexes, wherein the most representative components are refined as fault features by principal component analysis. Afterward, resorting to multiclass relevance vector machine, sparse classification models are constructed to cognize fault patterns, and accordingly, fault types and grades are evaluated. Common faults, including external and internal short-circuit, thermal abuse, and loose connection, are physically triggered on a series pack to acquire realistic data set. Experimental verifications under different conditions and algorithmic configurations suggest that the proposed diagnosis scheme can give accurate and reliable assessments on different fault specifics, with a fault isolation success rate of 84% and a fault severity grading success rate of 90%.

Multi-fault diagnosis for battery pack based on adaptive correlation sequence and sparse classification model

Aiming for an efficient fault diagnosis scheme for battery pack, this paper develops a complete framework for the diagnosis of faults in battery packs. First, an interclass sensor topology is introduced to cover multi-fault abnormalities, and an adaptive correlation coefficient between adjacent sensors is used to encompass system information. Then, the discrete wavelet packet transform (DWPT) is utilized to process the correlation sequences. Thereby, a variety of characteristic indicators are attained to and the main components are extracted by Principal Component Analysis (PCA) as fault features. Afterwards, two sparse classification models are developed, based on the multiclass relevance vector machine, to distinguish fault type and evaluate fault degree respectively. A fault injection platform is established to physically trigger the faults of external short, internal short, thermal abuse and loose connection on a series-connected four-cell pack. Finally, experimental verifications suggest that the proposed method gives accurate and reliable judgements on different fault types, and evaluates the fault degree accurately.

A fault diagnostic method for oil-immersed transformer based on multiple probabilistic output algorithms and improved DS evidence theory

Power transformer is one of the most vital equipment in the electric power systems, which serves as the connection of power networks at different voltage levels. However, the power transformers in service are subjected to various stresses like thermal stress and electrical stress, which may lead to several faults such as arching, partial discharge and overheating. In order to detect these incipient faults and ensure the normal operation of power transformers, the diagnostic methods based on Dissolved Gas Analysis (DGA) have been developed. However, the DGA methods suffer from low diagnostic accuracy. On the other hand, with the development of Artificial Intelligence (AI), a lot of intelligent algorithms have been applied in this area. However, most traditional AI algorithms are hard classification, which may cause the wrong diagnosis of faults. To overcome those disadvantages, a method based on the improved DS evidence theory and multiple probabilistic output algorithms is proposed in this paper to improve the performance of fault diagnosis of oil-immersed transformers. Firstly, the performance of the soft and hard classification algorithms is compared and analyzed. After that, three diagnostic models based on the Multiclass Relevance Vector Machine (MRVM), Multiclass Support Vector Machine (MSVM) and Back Propagation Neural Network (BPNN) are established. In order to improve the accuracy of these models, Particle Swarm Optimization (PSO) is used to optimize their hyper-parameters. Finally, the combination of these three probabilistic output models is achieved based on the improved Dempster-Shafer (DS) evidence theory, which can help to obtain a more accurate fault diagnosis result by fusing multiple algorithm results. The case study results show that the proposed method achieves a probabilistic output for the fault diagnosis of oil-immersed transformers and overcomes the deficiency of traditional DGA methods which are difficult to get accurate diagnosis results and can’t summarize the fault development rule inductively.

Quantified Assessment of Internal Short-Circuit State for 18 650 Batteries Using an Extreme Learning Machine-Based Pseudo-Distributed Model

To facilitate the diagnosis of battery internal short circuit (ISC) using thermal behaviors, this work integrates several thermal effects, including the commonly ignored heat conduction hysteresis and radiation, to elaborate a lumped thermal model. Then, a pseudo-distributed model structure is built up to approximate the characteristics of real batteries by synthesizing multiple isomorphic electrical/thermal submodels with the extreme learning machine network. Besides, three kinds of configurable destructions are conducted to incur ISC consequences. From thermal and electrical model residuals, four ISC features are extracted and the multiclass relevance vector machine is utilized to assess ISC intensity, in which not only qualitative judgments are given but also quantitative confidences can be derived according to the posterior probabilities. Finally, experiments on 18650 Li-ion cells verify the reliability of the synthesized models and suggest that the diagnosis scheme can recognize ISC faults effectively with low grade and state misjudgment rates (14.59% and 3.13%, respectively).

Quantitative diagnosis of internal short circuit for cylindrical li-ion batteries based on multiclass relevance vector machine

Battery internal short circuit (ISC) state should always be supervised. To facilitate the use of battery thermal behaviors, this work develops a lumped thermal evolution model (TEM) based on the equivalent circuit model (ECM). Then, multiple dispersedly-configured TEM/ECM sub-models are synthesized using the extreme learning machine to approximate the distribution nature of real batteries. To acquire ISC dataset, three kinds of active-destruction experiments are carried out on the battery. Thereafter, from thermal and electrical residuals, four ISC features are extracted whereby the multiclass relevance vector machine is utilized to discriminate ISC state. Specially, according to the posterior probability outputs, ISC degree can also be quantified. Experimental results on cylindrical li-ion batteries verify the reliability of the model structure and suggest the proposed diagnosis scheme can recognize ISC faults effectively with a grade misjudgment rate of 14.59% and a state misjudgment rate as low as 3.13%.

A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine.

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

A machine learning framework for genotyping the structural variations with copy number variant

BackgroundGenotyping of structural variation is an important computational problem in next generation sequence data analysis. However, in cancer genomes, the copy number variant(CNV) often coexists with other types of structural variations which significantly reduces the accuracy of the existing genotype methods. The bias on sequencing coverage and variant allelic frequency can be observed on a CNV region, which leads to the genotyping approaches that misinterpret the heterozygote as a homozygote. Furthermore, other data signals such as split mapped read, abnormal read will also be misjudged because of the CNV. Therefore, genotyping the structural variations with CNV is a complicated computational problem which should consider multiple features and their interactions.MethodsHere we proposed a computational method for genotyping indels in the CNV region, which introduced a machine learning framework to comprehensively incorporate a set of data features and their interactions. We extracted fifteen kinds of classification features as input and different from the traditional genotyping problem, here the structure of variant may fall into types of normal homozygote, homozygous variant, heterozygous variant without CNV, heterozygous variant with a CNV on the mutated haplotype, and heterozygous variant with a CNV on the wild haplotype. The Multiclass Relevance Vector Machine (M-RVM) was used as a machine learning framework combined with the distribution characteristics of the features.ResultsWe applied the proposed method to both simulated and real data, and compared it with the existing popular softwares include Gindel, Facets, GATK, and also compared with other machine learning cores: Support Vector Machine, Lanrange-SVM with OVO multiple classification, Naïve Bayes and BP Neural Network. The results demonstrated that the proposed method outperforms others on accuracy, stability and efficiency.ConclusionThis work shows that the genotyping of structural variations on the CNV region cannot be solved as a traditional genotyping problem. More features should be used to efficiently complete the five-category task. According to the result, the proposed method can be a practical algorithm to correct genotype structural variations with CNV on the next generation sequence data. The source codes have been uploaded at https://github.com/TrinaZ/Mixgenotypefor academic usage only.

Principle Component Analysis Based Data Mining for Contingency Analysis on IEEE 30 Bus Power System

This Paper is an attempt to develop a Data Mining tool for the contingency of the power system. By mining the big data in the power system and analyzing the early detection of the contingency in the power system a larger cost cutting can be planned. As Mining would reduce the computational complexity of the contingency analysis this attempt would lead to reduction in the hardware use. This paper uses Multiclass Relevance Vector Machine(MCRVM) and Multiclass Support vector machine(MCSVM) in order to mine the data which include the voltage, power generated , power angles , power demand in different lines of the power system. The Data mining would need a data transformation technique, which would reduce the dimensionality of the data introduced for mining. The combination of Data cleansing and the Principal Component Analysis would act as the data transformation technique in this paper. A Matlab based simulation is carried using the IEEE 30 bus system for the contingency analysis by incorporating the loading risk assessment strategy using the Multiclass SVM and RVM and the results are compared and the outputs are tabulated. Active power performance index and the reactive power performance index are used in contingency analysis of the IEEE 30 bus system thus used and the accuracy of classification and the speed of classification with the different methods and the contingency rankings are found and displayed.

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine.

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

Detection of the gas-bearing zone in a carbonate reservoir using multi-class relevance vector machines (RVM): comparison of its performance with SVM and PNN

Automatic classification of seismic reflection data plays an important role in reservoir characterization and petroleum geosciences. Recently, the relevance vector machines (RVM) have attracted substantial interest in data classification literature. The RVM is a Bayesian variant of support vector machine (SVM) that can surmount the limitations of other conventional methods. The main purpose of this study is to show the effectiveness of RVM as a linear classifier to classify the variation in seismic data. In this study, we explored the potentials of RVM in identifying anomalous seismic reflection signals. First, the efficiency of RVM technique is compared with other conventional methods (SVM and probabilistic neural network (PNN)), and then a multi-class RVM algorithm based on the Gaussian approximation model is used to successfully classify synthetic and field seismic data. The result of this study shows that RVM is successful in the determination of gas-bearing zone in one of the Iranian southern oil fields.

Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach

Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high-power applications in recent years. As power electronics equipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on the principle component analysis and the multiclass relevance vector machine (PCA-mRVM), is elaborated and proposed in this paper. First, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a fast Fourier transform is used to preprocess these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, an mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried out to highlight the proposed PCA-mRVM diagnosis performances.

Application of improved LMD, SVD technique and RVM to fault diagnosis of diesel valve trains

Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of singular value decomposition (SVD) technique, a new method based on improved local mean decomposition (LMD), SVD technique and relevance vector machine (RVM) was proposed for the identification of diesel valve fault in this study. Firstly, the vibration signals were acquired through the vibration sensors installed on the cylinder head in one normal state and four fault states of valve trains. Secondly, an improved LMD method was used to decompose the non-stationary signals into a set of stationary product functions (PF), from which the initial feature vector matrices can be formed automatically. Then, the singular values were obtained by applying the SVD technique to the initial feature vector matrixes. Finally, slant binary tree and sort separability criterion were combined to determine the structure of multi-class RVM, and the singular values were regarded as the fault feature vectors of RVM in the identification of fault types of diesel valve clearance. The experimental results showed that the proposed fault diagnosis method can effectively extract the features of diesel valve clearance and identify the diesel valve fault accurately.

Health condition identification of multi-stage planetary gearboxes using a mRVM-based method

Multi-stage planetary gearboxes are widely applied in aerospace, automotive and heavy industries. Their key components, such as gears and bearings, can easily suffer from damage due to tough working environment. Health condition identification of planetary gearboxes aims to prevent accidents and save costs. This paper proposes a method based on multiclass relevance vector machine (mRVM) to identify health condition of multi-stage planetary gearboxes. In this method, a mRVM algorithm is adopted as a classifier, and two features, i.e. accumulative amplitudes of carrier orders (AACO) and energy ratio based on difference spectra (ERDS), are used as the input of the classifier to classify different health conditions of multi-stage planetary gearboxes. To test the proposed method, seven health conditions of a two-stage planetary gearbox are considered and vibration data is acquired from the planetary gearbox under different motor speeds and loading conditions. The results of three tests based on different data show that the proposed method obtains an improved identification performance and robustness compared with the existing method.

A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns.

Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling (‘bagging’) for non-hierarchical multiclass classification. The method was tested on 120 cerebral 18fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration.

HUMAN ACTIVITY RECOGNITION BY FUSING MULTIPLE SENSOR NODES IN THE WEARABLE SENSOR SYSTEMS

This paper presents a novel method for recognizing human daily activity by fusion multiple sensor nodes in the wearable sensor systems. The procedure of this method is as follows: firstly, features are extracted from each sensor node and subsequently reduced in dimension by generalized discriminant analysis (GDA), to ensure the real-time performance of activity recognition; then, the reduced features are classified with the multiclass relevance vector machines (RVM); finally, the individual classification results are fused at the decision level, in consideration that the different sensor nodes can provide heterogeneous and complementary information about human activity. Extensive experiments have been carried out on Wearable Action Recognition Database (WARD). Experimental results show that if all the five sensor nodes are fused with the adaptive weighted logarithmic opinion pools (WLOGP) fusion rule, we can even achieve a recognition rate as high as 98.78%, which is far more better than the situations where only single sensor node is available or the activity data is processed by state-of-the-art methods. Moreover, this proposed method is flexible to extension, and can provide a guideline for the construction of the minimum desirable system.