Li Li

Bio: Li Li is an academic researcher. The author has contributed to research in topics: Support vector machine & Fault (power engineering). The author has an hindex of 1, co-authored 1 publications receiving 89 citations.

A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

Abstract: Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

Abstract: Since roller bearings are the key components in rotating machinery, detecting incipient failure occurring in bearings is an essential attempt to assure machinery operational safety. With a view to design a well intelligent system that can effectively correlate multiple monitored variables with corresponding defect types, a novel intelligent fault diagnosis method with multivariable ensemble-based incremental support vector machine (MEISVM) is proposed, which is testified on a benchmark of roller bearing experiment in comparison with other methods. Moreover, the proposed method is applied in the intelligent fault diagnosis of locomotive roller bearings, which proves the capability of detecting multiple faults including complex compound faults and different severe degrees with the same fault. Both experimental and engineering test results illustrate that the proposed method is effective in intelligent fault diagnosis of roller bearings from vibration signals.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

Abstract: The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.