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Kernel principal component analysis (KPCA) is a statistical analysis procedure that has been applied successfully for nonlinear process monitoring. However, it is still a challenging problem to determine the optimal kernel parameter during the KPCA modeling, which is very significant for both modeling and monitoring processes. In this paper, a new approach based on sample reconstruction is proposed...
There is an irregular data distribution when using multi-sensor to monitor reciprocating compressor conditions. It is difficult to deal with conventional approaches. In this paper an improved genetic algorithm based clustering method is used to solve the problem. First a prototype-based genetic representation is utilized, where each chromosome is a set of positive integer numbers that represent a...
Dynamic principal component analysis (DPCA) is required for the modeling and monitoring of dynamic processes. However, the root cause identification of faulty variables is quite desired after a fault is detected. As DPCA based methods construct detection indices in augmented variable space, it is difficult to use contribution analysis for diagnosis in a common way. In recent literature, reconstruction...
Quality-related fault detection attracted more and more attention in quality control and process monitoring. In recent literature, reconstruction based contributions (RBC) are used for isolating faulty variables which affect product quality. If datasets of known faults are available, fault-specific RBCs are used to identify fault types. Otherwise, variable RBCs are used to isolate faulty variables...
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...
Process monitoring is critical for efficient operations of industrial processes. When a fault occurs, relevant measured data are affected by the fault, which leads to poor quality of products consequently. This paper proposes a new output-relevant index for detecting faults that affect the output or quality, and studies the fault detectability based on total projection to latent structures (T-PLS)...
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