Fault Diagnosis Based on Principal Component Analysis and Support Vector Machine for Rolling Element Bearings

The Sixth International Symposium on Neural Networks (ISNN 2009)

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

The purpose of triage is to prevent the delay of treatment for patients in real emergencies due to excessive numbers of patients in the hospital. This study uses the data of patients of consistent triage to develop the triage prediction model. By integrating Principal Component Analysis (PCA) and Support Vector Machine (SVM), the anomaly detection (overestimate and underestimate) prediction accuracy rate can be 100%, which is better than the accuracy rate of SVM (about 89.2%) or Back- propagation Neural Networks (BPNN) (96.71%); afterwards, this study uses Support Vector Regression (SVR) to adopt Genetic Algorithm (GA) to determine three SVR parameters to predict triage. After using the scroll data predictive values, we calculate the Absolute Percentage Error (APE) of each scroll data. The resulting SVR's Mean Absolute Percentage Error (MAPE) is 3.78%, and BPNN's MAPE is 5.99%; therefore, the proposed triage prediction model of this study can effectively predict anomaly detection and triage.

Inversion of soil pH during the dry and wet seasons in the Yinbei region of Ningxia, China, based on multi-source remote sensing data

The profiles of volatile compounds emitted by plants varies in response to different damage. The potential of electronic nose technology to monitor such changes, with the aim of diagnosing plant health was investingted. An electronic nose(E-nose)was used to analyse tomato seedlings that were subjected to differnent types of damage(infection by Early blight disease, infection by Gray mold disease, mechanically damage, and undamaged). Principal component analysis(PCA), linear discrimination analysis(LDA), back-propagation neural network(BPNN), and support vector machine (SVM) network were used to evaluate the E-nose data. The results indicated that the E-nose can successfully discriminate between tomato seedling with different types of damage. The results of PCA and LDA showed that clusters of data were divided into 3 groups (ZP, HP, and CP/MP). Samples from groups CP and MP overlapped partially. Back-propagation neural network (BPNN) and support vector machine (SVM) network were used to evaluate the E-nose data. Good discrimination results were obtained using SVM and BPNN. The results demonstrate that it is plausible to use E-nose technology as a method for monitoring damage in tomato seedling.

On the basis of Laplaian score(LS) method, a supervised laplaian score(SLS) feature selection method is proposed. This method takes into account the data label information and local geometric structure, thus the problem of setting the neighbor graph parameters in LS method is avoided. Combined SLS with principal component analysis(PCA), a fault diagnosis method of rolling bearings is put forward. The feature of vibration signals of a rolling bearing is extracted in time domain and frequency domain, from which an initial feature vector is formed. By using SLS method to select features, fault feature vectors are obtained. Then, the PCA method is used to reduce the dimension of fault feature vectors and the K-nearest neighbor(KNN) method is used as a fault feature classifier to recognize different fault types of a rolling bearing. Application examples show that this method can be used to extract the features of vibration signals of rolling bearings and diagnosis the fault of rolling bearings effectively.

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding

Rolling bearings are essential parts, and its operation directly affects the overall condition of the equipment. Due to the complicated non-mapping relationship between the fault and the symptom issued, this paper adopts the strong non-mapping of the neural network and the ability to self-learn and adapt to the state detection and fault diagnosis of the rolling bearing. Through select the fault characteristic parameters, and the BP neural network to select normal operation and inner ring fault characteristic parameters for fault diagnosis. Experiments show that the BP neural network constructed in this paper can accurately determine whether the rolling bearing is fault based on actual data.

The location of logistic center directly influences the operational effect of the enterprise. Support vector machine (SVM) has been applied to regression widely. However, if the index of the training data has much noise and redundancy, the generalized performance of SVM will be weakened, so this can cause some disadvantages of slow convergence speed and low regression accuracy. A SVM regression model based on principal component analysis (PCA-SVM) is presented in this paper, using principal component analysis to reduce the dimensionality of indexes, and then extract principal components to replace the original indexes, and both processing speed and regression accuracy will be improved. At last, apply this model to logistic centre location, and it shows more generalized performance and better regression accuracy compared with the method of single SVM and BP neural networks.

Carbon emission is a significant indicator to balance regional development and environmental pollution. Currently, to achieve the global goal of carbon neutrality, many countries had formulated relevant policies to control carbon emissions in the future. It is of great importance to solve the problem of mid-term and long-term prediction of regional carbon emissions. To improve the prediction accuracy and lower the calculation cost, this paper uses the Principal Component Analysis (PCA) method to reasonably screen the driving factors of carbon emission prediction. It combines the Back Propagation (BP) neural network and Genetic Algorithm (GA) to construct the Genetic Algorithm - Back Propagation neural network (GA-BP) model for mid-term and long-term prediction. Based on historical statistics and official policy indicators, the prediction of carbon emissions in Jiangsu Province of China from 2025 to 2050 is carried out. By comparing the prediction results of the Multiple Linear Regression (MLR) model and the classic BP model, the improved GA-BP model based on Principal Component Analysis Stochastic Impacts by Regression on Population, Affluence, and Technology (PCA-STIRPAT) is significantly better than other methods in the mid-term and long-term prediction of regional carbon emissions, with a maximum relative error of 0.76% and a high prediction accuracy. This result can provide a reasonable basis for relevant parties to make decisions on controlling carbon emissions.