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Since reciprocating compressor (RC) is a key facility for many industries, the study of its fault diagnosis is thus particularly important. This paper proposes a new method for predicting the fault degree of RC by using a manifold learning method. The main idea of the proposed method can be summarized as follows: first, employ a manifold learning algorithm to directly deal with RC's cylinder pressure...
Aim at the problem that it is difficult to detect reciprocating compressor early fault data with complex shape clusters, a novel fault detection algorithm is put forward based on antibody clonal selection and immune memory principle. Firstly, high dimension space of raw feature signals is constructed by multivariate statistical analysis, and then the local tangent space alignment (LTSA) algorithm...
Large amount of multivariate data in many areas of science raises the problem of data analysis and visualization. Focusing on high dimensional and nonlinear data analysis, an improved manifold learning algorithm is introduced, then a new approach is proposed by combining adaptive local linear embedding (ALLE) and recursively applying normalized cut algorithm (RANCA). A novel adaptive local linear...
Aim at the problem that classical Euclidean distance metric cannot generate a appropriate partition for data lying in a manifold, a genetic algorithm based clustering method using geodesic distance measure is put forward. In this study, a prototype-based genetic representation is utilized, where each chromosome is a sequence of positive integer numbers that represent the k-medoids. Additionally, a...
Analysis of large amount of data is needed in many areas of science, and this depends on dimensionality reduction of the multivariate data. Local linear embedding (LLE) is efficient for many nonlinear dimension reduction problems because of its low computation complexity and high efficiency, however LLE often leads to invalidation in the event that the data is sparse or noise contaminated. In order...
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