A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems

Simmini, Francesco; Rampazzo, Mirco; Peterle, Fabio; Susto, Gian Antonio; Beghi, Alessandro

doi:10.1109/tcst.2021.3107200

Cited by 21 publications

(11 citation statements)

References 52 publications

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“…. , a L ) can be calculated and present a new direction to reduce the dimensionality of high-dimensional data [21], as shown below:…”

Section: Kernel Principal Component Analysis (Kpca)mentioning

confidence: 99%

“…Note that the eigenvalues are corresponding to the kernel matrix K and then arrange them in order from the largest to the smallest. The eigenvalues L with the highest contribution rate and the corresponding eigenvectors (a1, a2, …, aL) can be calculated and present a new direction to reduce the dimensionality of high-dimensional data [21], as shown below:…”

Section: Learning Vector Quantization Neural Network (Lvqnn)mentioning

confidence: 99%

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Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Jiang

Gan

et al. 2021

WEVJ

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To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

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“…. , a L ) can be calculated and present a new direction to reduce the dimensionality of high-dimensional data [21], as shown below:…”

Section: Kernel Principal Component Analysis (Kpca)mentioning

confidence: 99%

Section: Learning Vector Quantization Neural Network (Lvqnn)mentioning

confidence: 99%

Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Jiang

Gan

et al. 2021

WEVJ

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“…Semi-supervised learning is often used by implementing a Generative Adversarial Network (GAN), which can provide a larger amount of data and classes balance with generating a synthetic dataset [ 43 , 44 , 54 , 63 , 102 ]. The single-class (binary) classifier (SVM) is also a popular method, in which a model is trained only on healthy datasets that have been generated by GAN [ 44 , 66 ].…”

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

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“…According to the difficulty level for faults to detect, they can be roughly divided into three categories as follows. The first includes the faults with large magnitudes: IDV(1), IDV(2), IDV(6)-IDV (8), IDV(12)-IDV (14), IDV (17), IDV (18), which are usually easily detectable. The second contains the 1 http://depts.washington.edu/control/LARRY/TE/download.html Step IDV (4) Reactor cooling water inlet temperature…”

Section: Dae-pca Based Nonlinear Fault Detectionmentioning

confidence: 99%

“…Owing to its good nonlinear feature extraction capability, KPCA and its extensions have been widely used for various fault detection and multivariate statistical process monitoring (MSPM) tasks of industrial processes currently [14]- [16]. Simmini et al proposed a self-tuning KPCA method to detect the faults in chiller systems with a high accuracy [17]. Fan et al presented a fast incremental nonlinear matrix completion method, which can make KPCA successfully monitor nonlinear processes when there exist missing data [18].…”

Section: Introductionmentioning

confidence: 99%

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

Ren¹,

Yang²,

Jiang³

et al. 2021

Preprint

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Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick-based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial-and-error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high-dimensional space according to the inputs. Furthermore, the online computational efficiency improves by approximately 100 times compared with the conventional KPCA. With the Tennessee Eastman (TE) process benchmark, the effectiveness and superiority of the proposed method is illustrated.

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A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems

Cited by 21 publications

References 52 publications

Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

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