A Cumulative Canonical Correlation Analysis-Based Sensor Precision Degradation Detection Method

Chen, Zhiwen; Yang, Chunhua; Peng, Tao; Huang, Dan; Li, Changgeng; Gui, Weihua

doi:10.1109/tie.2018.2873100

Cited by 66 publications

(23 citation statements)

References 35 publications

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“…In abc static coordinate frame, every work period of the three-phase four-switch converter is divided into four sectors by four basic space voltage vectors. The sector formed by two adjacent basic space voltage vectors V z and V z+1 is named as N z , where (z, z + 1) can be (1, 2), (2, 3), (3,4) and (4, 1), as shown in Figure 2. The voltage state equation of the motor emulator can be expressed as:…”

Section: Modeling Of Motor Emulatormentioning

confidence: 99%

“…As key power equipment, motors are widely used in various applications such as defense military, industrial production, and rail transit [1][2][3]. In the past few decades, the research about motor operating on fault state and condition suddenly alteration have become hot topics in high voltage and power applications [4,5]. The physical motors in the experiments of motor faults are usually damaged, which are not beneficial for performing experiments repeatedly and the cost of the experiments is high.…”

Section: Introductionmentioning

confidence: 99%

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A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count

et al. 2019

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This paper presents a power loss decrease method based on finite set model predictive control (FSMPC) with delay compensation for a motor emulator with reduced switch count. Specifically, the topology and mathematical model of the proposed motor emulator with reduced switch count are firstly built. Secondly, in light of given instructions, the normal or fault reference current of the motor emulator is set by a reference current setter. Then delay compensation is applied for the predictive current model to calculate the current residual generated by each switch control signal, and the current tracking performance under actions of two adjacent switch control signals is evaluated for each sector. Finally, a switch power loss objective function is defined, then the two adjacent switch control signals that generate the lowest switch power loss are selected for the next second instant, which minimizes the power loss of the motor emulator with ensuring satisfied current tracking performance. Simulation and experimental results show the feasibility and effectiveness of the proposed method.

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Section: Modeling Of Motor Emulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count

et al. 2019

Self Cite

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“…The concept of information fusion was first proposed in early 1970s [18]. CCA is a kind of fusion algorithm and it has been applied in many fields, such as: pattern recognition [25]- [27], image processing and analysis [28], signal processing [29]- [31], and so on. For multi-group features learning, classical single-view learning methods, such as principal component analysis, can not effectively extract complete features.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. INDEX TERMS Nonlinear analog circuit fault diagnosis, WPT, RCCA-SVM.

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“…Wang proposed [14] a novel statistical model based on locality-preserving PLS to improve the nonlinear processing capability of the system. Chen proposed an improved canonical correlation analysis (CCA) to realize the detection of multiplication faults in industrial processes [15] . Peng proposed [16] the combination of just-in-time learning and extreme learning machine (ELM) to deal with non-Gaussian chemical processes.…”

Section: Introductionmentioning

confidence: 99%

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

Peng

Tian

2020

IEEE Access

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In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

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A Cumulative Canonical Correlation Analysis-Based Sensor Precision Degradation Detection Method

Cited by 66 publications

References 35 publications

A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count

A Power Loss Decrease Method Based on Finite Set Model Predictive Control for a Motor Emulator with Reduced Switch Count

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

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