Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Fault detection (FD) for non-Gaussian stochastic systems with time-varying delay is studied. The available information for the addressed problem is the input and the measured output probability density functions (PDFs) of the system. In this framework, firstly, by constructing an augmented Lyapunov functional, which involves some slack variables and a tuning parameter, a delay-dependent condition for the existence of FD observer is derived in terms of linear matrix inequality (LMI) and the fault can be detected through a threshold. Secondly, in order to improve the detection sensitivity performance, the optimal algorithm is applied to minimize the threshold value. Finally, paper-making process example is given to demonstrate the applicability of the proposed approach.

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Minimum entropy fault detection for dynamic multivariate nonlinear non-Gaussian stochastic systems

2012 24th Chinese Control and Decision Conference (CCDC) ◽

10.1109/ccdc.2012.6244645 ◽

2012 ◽

Author(s):

Liping Yin ◽

Min Zhang

Keyword(s):

Fault Detection ◽

Stochastic Systems ◽

Minimum Entropy ◽

Non Gaussian

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Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-05822-1 ◽

2021 ◽

Author(s):

K Ramakrishna Kini ◽

Muddu Madakyaru

Keyword(s):

Fault Detection ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component ◽

Noisy Environment ◽

Process Data ◽

Multi Scale ◽

Efficient Information ◽

Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

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