Lifei Liu

Bio: Lifei Liu is an academic researcher from Harbin University of Science and Technology. The author has contributed to research in topics: Bayesian inference & Computational complexity theory. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Fault severity identification of roller bearings using flow graph and non-naive Bayesian inference:

Abstract: In order to address the problem that redundant condition attribute nodes and poor reasoning ability of flow graph may lead to high computational burden and low diagnosis accuracy, a fault severity identification method of roller bearings using flow graph and non-naive Bayesian inference is put forward in this paper. First, a normalized flow graph constructed according to fault features of roller bearings extracted from training samples is used to represent and describe the causal relationship among attributes. Then, the significance degree of condition attribute node with respect to the decision attribute node set is defined to quantitatively measure the impact of the node on the decision-making abilities of the flow graph. A node reduction algorithm based on significance degree of condition attribute node is developed to delete redundant or irrelevant condition attribute nodes, which can improve clustering distribution and reduce computational complexity. Finally, non-naive Bayesian inference is utilized...

Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System

Abstract: Traditional feature extraction methods are used to extract the features of signal to construct the fault feature matrix, which exists the complex structure, higher correlation, and redundancy. This will increase the complex fault classification and seriously affect the accuracy and efficiency of fault identification. In order to solve these problems, a new fault diagnosis (PABSFD) method based on the principal component analysis (PCA) and the broad learning system (BLS) is proposed for rotor system in this paper. In the proposed PABSFD method, the PCA with revealing the signal essence is used to reduce the dimension of the constructed feature matrix and decrease the linear feature correlation between data and eliminate the redundant attributes in order to obtain the low-dimensional feature matrix with retaining the essential features for the classification model. Then, the BLS with low time complexity and high classification accuracy is regarded as a classification model to realize the fault identification; it can efficiently accomplish the fault classification of rotor system. Finally, the actual vibration data of rotor system are selected to test and verify the effectiveness of the PABSFD method. The experimental results show that the PCA method can effectively eliminate the feature correlation and realize the dimension reduction of the feature matrix, the BLS can take on better adaptability, faster computation speed, and higher classification accuracy, and the PABSFD method can efficiently and accurately obtain the fault diagnosis results.

Fault Diagnosis in Gas Insulated Switchgear Based on Genetic Algorithm and Density- Based Spatial Clustering of Applications With Noise

Abstract: As a kind of widely used switchgear in power system, the reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, there is a lack of research on intelligent detection technology of mechanical state of GIS at present. A new method is urgently needed to improve the operability, effectiveness, and accuracy of fault detection in GIS. Aiming at the abnormal vibration signals generated by GIS faults, this article presents a fault diagnosis method (GA-DBSCAN) consisting of a feature selection method based on genetic algorithm (GA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and a fault diagnosis method based on DBSCAN. First, this article analyzes the incentive force of GIS and discusses the characteristic frequency of response signal combining with the non-linear characteristics of a GIS system. Second, GA and DBSCAN are used to screen features for dimension reduction and get the optimized feature space, and DBSCAN-based classification is used to classify faults. Finally, optimized feature space is verified to be superior to the original feature space by typical classification method; the superiority and reliability of DBSCAN-based classification method under optimized feature space is verified by comparing with other classification methods. The proposed GA-DBSCAN approach can substantially increase the performance of the fault diagnosis method, which indicates that the method promotes development of intelligent detection technology of mechanical state in GIS.

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Abstract: The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

A novel fault diagnosis method based on improved adaptive variational mode decomposition, energy entropy, and probabilistic neural network

Abstract: To improve the accuracy of bearing fault recognition, a novel bearing fault diagnosis (PAVMD-EE-PNN) method based on parametric adaptive variational mode decomposition (VMD), energy entropy, and pr...