A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis

Feng, Kun; Jiang, Zhinong; He, Wei; Ma, Bo

doi:10.1016/j.eswa.2011.04.060

Cited by 51 publications

(21 citation statements)

References 21 publications

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“…This will not only increase the computational complexity, but also decrease the performance [19]. Various techniques have been developed for reducing the dimensionality of the feature space in the hope of obtaining a more manageable problem [20].…”

Section: Reduction Of Data Dimensionmentioning

confidence: 99%

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Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model

Giurgea

Outbib

et al. 2014

Fuel Cells

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This paper deals with the online fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) stack. In the proposed approach, support vector machine (SVM) and a fluidic model are correlated to realize the diagnosis of the faults on water management. With the help of the fluidic model, health states of the stack can be identified. This procedure is then dedicated to labeling the training data, which is used to train the dimension reduction model, named Fisher discriminant analysis (FDA), and the SVM classifier. The online diagnosis can then be realized by using the trained FDA and SVM models. The proposed approach is illustrated by using the experimental data of a 20‐cell PEMFC stack. The feasibility of the approach for online implementation is also affirmed.

show abstract

Section: Reduction Of Data Dimensionmentioning

confidence: 99%

“…After solving the QP problem in Eqs. (19) and (20), the samples corresponding to a n > 0 are SVs, which are denoted as z s 1 , z s 2 , ... , z s S . The corresponding a n and g n are denoted by a s n and g s n .…”

Section: Classification By Svmmentioning

confidence: 99%

Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model

Giurgea

Outbib

et al. 2014

Fuel Cells

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show abstract

“…Similarly, for the inter-class graph, the negative element will transform the objective function (28) into:…”

Section: N and D Is Thementioning

confidence: 99%

Fault diagnosis of sucker rod pumping systems based on Curvelet Transform and sparse multi-graph regularized extreme learning machine

Zhang¹,

Gao²

2018

IJCIS

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A novel approach is proposed to complete the fault diagnosis of pumping systems automatically. Fast Discrete Curvelet Transform is firstly adopted to extract features of dynamometer cards that sampled from sucker rod pumping systems, then a sparse multi-graph regularized extreme learning machine algorithm (SMELM) is proposed and applied as a classifier. SMELM constructs two graphs to explore the inherent structure of the dynamometer cards: the intra-class graph expresses the relationship among data from the same class and the inter-class graph expresses the relationship among data from different classes. By incorporating the information of the two graphs into the objective function of extreme learning machine (ELM), SMELM can force the outputs of data from the same class to be as same as possible and simultaneously force results from different classes to be as separate as possible. Different from previous ELM models utilizing the structure of data, our graphs are constructed through sparse representation instead of K-nearest Neighbor algorithm. Hence, there is no parameter to be decided when constructing graphs and the graphs can reflect the relationship among data more exactly. Experiments are conducted on dynamometer cards acquired on the spot. Results demonstrate the efficacy of the proposed approach for faults diagnosis in sucker rod pumping systems.

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“…Upon performing dimensionality reduction on the data, its compact representation can be utilized for succeeding tasks (e.g., visualization and classification). Among various dimensionality reduction methods [16][17][18][19][20][21][22][23][24], principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most common methods [21]. The former is an unsupervised method, which pursues the direction of maximum variance for optimal reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

Yang

Xiao

Zhou

et al. 2016

Shock and Vibration

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The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.

show abstract

A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis

Cited by 51 publications

References 21 publications

Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model

Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model

Fault diagnosis of sucker rod pumping systems based on Curvelet Transform and sparse multi-graph regularized extreme learning machine

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

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