Sensor Multifault Diagnosis With Improved Support Vector Machines

Deng, Fang; Guo, Sen; Zhou, Rui; Chen, Jie

doi:10.1109/tase.2015.2487523

Cited by 138 publications

(52 citation statements)

References 23 publications

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“…dimension reduction: Based on random projection, the original data set is projected into the lower dimension space according to equation (5) to obtain the projected matrixX ¼ ½x 1 ;x 2 ; Á Á Á ;x M 2 R dÂM , where d denotes the dimension of the low dimensional space; 17 2. cutting: cut two-dimensional dataX along the time axis into M data points,x i ; i ¼ 1; 2; Á Á Á ; M; 3. subperiod division: The M data pointsx i are taken as the input samples of the cluster, and the process is divided into K stages according to the principle of SSE. 4. adjustment: The input of the clustering algorithm is arranged in chronological order, and the consistency of the process variables is divided accordingly.…”

Section: Subperiod Division Of Batch Processmentioning

confidence: 99%

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

Wen

Zhou

et al. 2017

International Journal of Advanced Robotic Systems

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As an important production method, the batch process is complex and flexible. Moreover, the modeling complexity and the spatial complexity of the storage model are higher, and the monitoring of the actual batch process is more difficult. To address this problem, this article proposes a fault detection method based on random projection, K-means clustering, and the k-nearest neighbor algorithm. First, a multiperiod division method is put forward based on the random projection and the K-means clustering algorithm. This reduces the computational complexity while ensuring the fault detection performance of the algorithm. Second, a real-time monitoring model is established based on each sub-period data using the k-nearest neighbor method to realize online monitoring of the batch production process. According to the premise that the fault detection performance is approximately equal, the proposed method reduces the complexity and computation of the model and realizes the real-time demand of fault detection.

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Section: Subperiod Division Of Batch Processmentioning

confidence: 99%

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

Wen

Zhou

et al. 2017

International Journal of Advanced Robotic Systems

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“…Additionally, the Cohen's Kappa [46] index is used to measure [47,48] the agreement between the proposed method and the group of physicians. The index (Kappa ¼ 0.867 and P < 0.05) indicates that the proposed method has a high consistency with the doctors' diagnosis.…”

Section: Locations Of Key Elementsmentioning

confidence: 99%

A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis

Sangwoo

Gao

Xie

et al. 2017

Computer Assisted Surgery

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Traditional dysphagia prescreening diagnostic methods require doctors specialists to give patients a total score based on a water swallow test scale. This method is limited by the high dimensionality of the diagnostic elements in the water swallow test scale with heavy workload (Towards each patient, the scale requires the doctors give score for 18 diagnostic elements respectively) as well as the difficulties of extracting and using the diagnostic scale data's non-linear features and hidden expertise information (Even with the scale scores, specific diagnostic conclusions are still given by expert doctors under the expertise). In this paper, a hybrid classifier model based on Nonlinear-Principal Component Analysis (NPCA) and Deep Belief Networks (DBN) is proposed in order to effectively extract the diagnostic scale data's nonlinear features and hidden information and to provide the key scale elements' locating methods towards the diagnostic results (The key scale elements that affect different diagnostic conclusions are given to improve the efficiency and pertinence of diagnosis and reduce the workload of diagnosis). We demonstrate the effectiveness of the proposed method using the frame of 'information entropy theory'. Real dysphagia diagnosis examples from the China-Japanese Friendship Hospital are used to demonstrate applications of the proposed methods. The examples show satisfactory results compared to the traditional classifier.

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“…The uncertainty of the actual engineering system has been studied in many works [1][2][3][4]. The neural networks [5] and fuzzy logic systems [6] have become the two main tools which can effectively deal with the unknown functions in the systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

2018

Complexity

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This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with timevarying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

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Sensor Multifault Diagnosis With Improved Support Vector Machines

Cited by 138 publications

References 23 publications

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

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