Variable Predictive Model-based Class Discrimination Research Articles

Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

A novel feature selection method to boost variable predictive model–based class discrimination performance and its application to intelligent multi-fault diagnosis

Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. Variable predictive model–based class discrimination is a recently developed multiclass discrimination method and has been proved to be potential tool for multi-fault detection. However, the vibration signals from dynamic mechanical system always present non-normal distribution so that the original variable predictive model–based class discrimination might produce the inaccurate outcomes. An improved variable predictive model–based class discrimination method is introduced at first in this work. At the same time, variable predictive model–based class discrimination will suffer computation difficulty in the case of high-dimension input features. Therefore, a novel feature selection method based on similarity-fuzzy entropy is presented to boost the performance of the variable predictive model–based class discrimination classifier. In this method, the ideal feature vectors are optimized to acquire more accurate similarity-fuzzy entropies for the input features. And, the one with the largest similarity-fuzzy entropy value is removed to refine input feature subset. Moreover, the optimal input features are repeatedly evaluated using the improved variable predictive model–based class discrimination classifier until the expected results are achieved. Finally, the incipient multi-fault diagnosis model for a hydraulic piston pump is established and verified by experimental test. Some comparisons with commonly used methods were made, and the results indicate that the proposed method is more effective and efficient.

A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition

At present, the multiscale fuzzy entropy has been verified to be an excellent measure of the complexity for dynamic time series. However, when using to short-time time series collected in practical application, the conventional multiscale fuzzy entropy may result in undefined or unreliable value. In this work, improved multiscale fuzzy entropy, named moving-average based multiscale fuzzy entropy (MA_MFE), is presented at first to potentially characterize the complexity of short-term time series. The MA_MFE algorithm can successfully produce more template vectors to overcome the problem of shortening the samples in the procedure of the existing approaches. The analysis experiments for both white noise signal and 1/f noise signal are made and the results show MA_MFE method is more effective for the short-term datasets. Then, a novel fault detection scheme has been developed. After using non-local mean approach to reduce background noise, the non-stationary vibration signals are decomposed into several intrinsic scale components (ISCs) by a newly developed time-frequency signal analysis method- partly ensemble local characteristic-scale decomposition (PELCD); The ISCs with higher correlation coefficients are used to reconstruct into a new signal and the inherent MA_MFEs are extracted to quantify the complexity of the collected vibration signal. At last, the multiSVM and improved variable predictive model based class discrimination (VPMCD) are employed as small-sample classifiers to achieve fault detection. Two experiments have been conducted, which include both rolling bearing as vital component in rotating machinery and a piston pump as typical reciprocation machinery in hydraulic system. The comparison results show that the proposed fault detection scheme is more effective and reliable and suitable for real-time online fault detection.

Partial discharge pattern recognition using variable predictive model‐based class discrimination with kernel partial least squares regression

The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter-relations of the extracted features of PD signals. In fact, the inter-relations can show distinct dissimilarities between different classes of the signals. To overcome the defect of existing pattern recognition methods, the variable predictive model-based class discrimination (VPMCD), a new pattern recognition method, is introduced for the pattern recognition of PD in this study. However, the original VPMCD lacks general expression ability of the inter-relations of extracted features and inherits the shortcomings of least squares (LS) regression. To overcome the above defects, an improved VPMCD based on kernel partial LS (KPLS) regression, which is called as KPLS-VPMCD, is proposed in this study. Experiments and analyses are implemented using both UCI datasets and the extracted features of PD signals. The experiments show that the performance of the proposed KPLS-VPMCD is better than those of the existing methods such as VPMCD, back propagation neural networks, and support vector machines. The conclusion is that KPLS-VPMCD is an efficient supervised learning algorithm with consistency and good performance for PD pattern recognition.

A NOVEL APPROACH FOR THE PATTERN RECOGNITION OF HAND MOVEMENTS BASED ON EMG AND VPMCD

Essentially, the classification of human hand movements is a process of pattern recognition. However, existing computationally intense and complex pattern recognition methods have failed thus far to be optimally successful in constructing associations between extracted signal features. Due to such limitations, a new pattern recognition method using variable predictive model-based class discrimination (VPMCD) is proposed. This approach considers that the feature values can exhibit inter-relations in nature and such associations will show different forms in different classes. In practice, this is always true for different hand movements. The signals produced by electromyography (EMG) and received from human arm muscles, are characteristically non-linear and non-stationary. A novel hand gesture recognition technique, based on wavelet feature extraction and VPMCD is proposed. First, the maximum values of the wavelet coefficient are extracted as the feature vectors from the surface EMG signals after de-noising. Then, the feature values are regarded as the inputs of the VPMCD classifier. Finally, four movement patterns (hand clenching, hand extension, wrist flexion, and wrist extension) are identified by the outputs of the VPMCD classifier. Our analysis results show that the proposed pattern recognition approach can distinguish different gestures successfully and effectively. Simultaneously, compared with the artificial neural network and the support vector machine classifier, more accurate recognition can be achieved using our proposed technique.

Application of VPMCD method based on PLS for rolling bearing fault diagnosis

To address the non-stationary and nonlinear characteristics of vibration signals produced by rolling bearings and the noise pollution of acquired signals, a fault diagnosis method based on singular value decomposition (SVD), empirical mode decomposition (EMD) and variable predictive model-based class discrimination (VPMCD) is proposed in this paper. VPMCD is a novel pattern recognition method; however, according to the results obtained when the fault diagnosis method is applied to a small sample, the stability of the VPM constructed based on the least squares (LS) method is not sufficient, as demonstrated by the multiple correlations found between independent variables. This paper uses the partial least squares (PLS) method instead of the LS method to estimate the model parameters of VPMCD. Compared with the back-propagation neural network (BP-NN) and least squares support vector machine (LS-SVM) methods, based on numerical examples, the method presented in this paper can effectively identify a faulty rolling bearing.

Partial discharge pattern recognition method based on variable predictive model‐based class discriminate and partial least squares regression

Both the feature extraction method and pattern recognition method are of great importance to assess the health condition of a power transformer. Since the partial discharge (PD) signals of the transformer are non-stationary and non-linear, and the existing pattern recognition methods fail to capitalise on the inter-relations between the extracted features of the signals, a novel pattern recognition method, namely variable predictive model based class discrimination (VPMCD) is introduced for the PD pattern recognition in this research. However, the parameters of VPMCD are estimated by using least squares (LS) regression which is sensitive to multiple correlations between independent variables. Fortunately, partial LS (PLS) regression is usable even if features are highly correlated or the number of trained samples is very small. It is novelly adopted to overcome the defects of LS regression. Therefore, an automatic PD source classifier based on PLS and VPMCD, i.e. PLS-VPMCD, is put forward in this study. PD signal features from either pulse shape characterisations or phase-resolved PD are extracted. Then, the features are used as the input vectors of PLS-VPMCD classifier. PD signals sampled from four artificial defect models are adopted for the algorithms testing. Compared with the original VPMCD and back propagation recognition methods, PLS-VPMCD has much higher recognition accuracy.

Internal defect inspection in magnetic tile by using acoustic resonance technology

This paper focuses on the validity of a nondestructive methodology for magnetic tile internal defect inspection based on acoustic resonance. The principle of this methodology is to analyze the acoustic signal collected from the collision of magnetic tile with a metal block. To accomplish the detection process, the separating part of the detection system is designed and discussed in detail in this paper. A simplified mathematical model is constructed to analyze the characteristics of the impact of magnetic tile with a metal block. The results demonstrate that calculating the power spectrum density (PSD) can diagnose the internal defect of magnetic tile. Two different data-driven multivariate algorithms are adopted to obtain the feature set, namely principal component analysis (PCA) and hierarchical nonlinear principal component analysis (h-NLPCA). Three different classifiers are then performed to deal with magnetic tile classification problem based on features extracted by PCA or h-NLPCA. The classifiers adopted in this paper are fuzzy neural networks (FNN), variable predictive model based class discrimination (VPMCD) method and support vector machine (SVM). Experimental results show that all six methods are successful in identifying the magnetic tile internal defect. In this paper, the effect of environmental noise is also considered, and the classification results show that all the methods have high immunity to background noise, especially PCA-SVM and h-NLPCA-SVM. Considering the accuracy rate, computation cost problem and the ease of implementation, PCA-SVM turns out to be the best method for this purpose.

Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

An automatic fault diagnosis method for rolling bearing is proposed in this paper. Partially ensemble empirical mode decomposition (PEEMD) is developed to solve the problem of mode mixing existing in empirical mode decomposition. Compared with the ensemble empirical mode decomposition, PEEMD generates much more accurate intrinsic mode functions (IMFs) and the decomposing results are complete and orthogonal. Therefore, PEEMD is utilized to preprocess the vibration signals of rolling bearing. Moreover, the features in time, frequency domains of IMFs and ones of original data in time–frequency domain are extracted to reflect the change of fault information. To avoid the high dimension of features, Laplacian score for feature selection is utilized to sort the initial features according to their significances. The pattern recognition method, variable predictive model-based class discrimination (VPMCD) is introduced to achieve an automatic fault diagnosis. Finally, the proposed fault diagnosis method for rolling bearing is applied to analyze the experimental data and the result indicates that the proposed method can effectively diagnose the fault categories and severities of rolling bearings.

An intelligent fault diagnosis model for rotating machinery based on multi-scale higher order singular spectrum analysis and GA-VPMCD

Feature extraction and class discrimination are two key problems for fault diagnosis of rotating machinery. Firstly, multi-scale higher order singular spectrum analysis (MS-HO-SSA) method is presented and the multi-scale higher order singular spectrum entropy (MSHOSSE) is defined as feature to reveal the non-Gaussian and nonlinear characteristic for the vibration signals from rotating machinery with local faults. Secondly, GA-VPMCD method is presented by combination genetic algorithm (GA) with conventional variable predictive model based class discriminate (VPMCD) approach. Lastly, an intelligent fault diagnosis model based on MS-HO-SSA and GA-VPMCD is put forward and utilized for rotor fault diagnosis. The experimental results show that MS-HO-SSA method is more effective for feature extraction and the GA-VPMCD provides better performance than conventional VPMCD and LSSVM.

A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD) can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD) technique based local characteristic-scale decomposition (LCD) was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN) and mean impact value (MIV), ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

A fault diagnosis approach for roller bearing based on improved intrinsic timescale decomposition de-noising and kriging-variable predictive model-based class discriminate

The measured vibration signal of roller bearings has noise signals, which will largely influence the accuracy of roller bearing fault diagnosis. This paper puts forward a vibration signal de-noising method based upon improved intrinsic timescale decomposition (ITD); fuzzy entropy is then extracted as the fault feature of the roller bearing. Essentially the fault diagnosis of roller bearings is a process of pattern recognition. Targeting the limitation of existing pattern recognition methods, a new pattern recognition method – variable predictive model-based class discriminate (VPMCD) – is introduced into roller bearing fault identification. In the original VPMCD classifier, however, only the regression model could be used to predict, which will reduce the prediction’s accuracy when the relation between features is complicated. Aimed at this defect, a kriging-variable predictive model-based class discriminate (KVPMCD) pattern recognition method is presented in this paper. The kriging model is composed of a regression model and a correlation model, of which the correlation model is a local deviation that is created on the basis of a global model, to make up for the shortcomings of the simple regression model in the original VPMCD. Therefore, a fault diagnosis approach for roller bearing based on improved ITD de-nosing and KVPMCD is proposed in this paper. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can accurately and effectively distinguish the status of roller bearings with or without fault and fault patterns.

A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination

A new rolling bearing fault diagnosis method based on multi-scale fuzzy entropy (MFE), Laplacian Score (LS) and variable predictive model-based class discrimination (VPMCD) is proposed in this paper. Compared with previous approximate entropy (ApEn) and sample entropy (SampEn), MFE has taken into account the dynamic nonlinearity, interaction and coupling effects among mechanical components and thus it provides much more hidden information in different scales of vibration signal. Hence, MFE is employed to characterize the complexity and irregularity of rolling bearing vibration signals. Besides, to fulfill an automatical fault diagnosis, the VPMCD, as a new classification approach, is employed to construct a multi-fault classifier for making decision. Also, Laplacian Score (LS) for feature selection is utilized to refine the feature vector by sorting the features according to their importance and correlations with the fault information to eschew a high dimension of feature vector. Finally, the proposed method is implemented to rolling bearing experimental data and the results indicate that the proposed method is able to discriminate the different fault categories and degrees effectively.

A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition

Essentially the fault diagnosis of roller bearing is a process of pattern recognition. However, existing pattern recognition method failed to capitalize on the nature of multivariate associations between the extracted fault features. Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. The VPMCD consider that all or part of the feature values will exhibit interactions in nature and these associations will have different performances between different classes, which is always true in practice when faults occur in roller bearings. Target to the characteristics of non-stationary and amplitude-modulated and frequency-modulated (AM–FM) of vibration signal picked up under variable speed condition, a fault diagnosis method based upon the VPMCD, order tracking technique and local mean decomposition (LMD) is put forward and applied to the roller bearing fault identification. Firstly, LMD and order tracking analysis method are combined to extract the fault features of roller bearing vibration signals under variable speed condition; Secondly, the feature values are regard as the input of VPMCD classifier; finally, the working condition and fault patterns of the roller bearings are identified automatically by the output of VPMCD classifier. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can distinguish the roller bearing status-with or without fault and fault patterns under variable speed condition accurately and effectively.

Variable predictive models—A new multivariate classification approach for pattern recognition applications

Many pattern recognition algorithms applied in literature exhibit data specific performances and are also computationally intense and complex. The data classification problem poses further challenges when different classes cannot be distinguished just based on decision boundaries or conditional discriminating rules. As an alternate to existing methods, inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Based on this idea, variable predictive model based class discrimination (VPMCD) method is proposed as a new and alternative classification approach. Analysis is carried out using seven well studied data sets and the performance of VPMCD is benchmarked against well established linear and non-linear classifiers like LDA, kNN, Bayesian networks, CART, ANN and SVM. It is demonstrated that VPMCD is an efficient supervised learning algorithm showing consistent and good performance over these data sets. The new VPMCD method has the potential to be effectively and successfully extended to many pattern recognition applications of recent interest.

Variable predictive model based classification algorithm for effective separation of protein structural classes

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25–100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.

VPMCD: Variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.