Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters

Wu, Jianguo; Chen, Yong; Zhou, Shiyu; Li, Xiaochun

doi:10.1109/tase.2014.2378150

Cited by 32 publications

(27 citation statements)

References 33 publications

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“…The same rule in Ref. [16] is applied and p 1 ¼ p 2 ¼ 0.3 is chosen for transition probabilities to make a fair comparison. For simplicity, q is also chosen to be 0.3.…”

Section: Resultsmentioning

confidence: 99%

“…Wu's Paper. The proposed method is compared with the recent method from Wu's paper [16]. Wu et al proposed a piecewise linear state-space model to formulate the multiple change point detection problems and used particle filter to estimate the latest change point.…”

Section: Comparison With the Methods Frommentioning

confidence: 99%

“…A typical model in this category is the hidden Markov model. Examples can be found in Transactions of the ASME the field of advanced manufacturing, such as the ultrasoniccavitation-based nanoparticle dispersion process [16][17][18]. This model is effective if the latent process variables and the states transition mechanisms are known.…”

Section: Review Of Related Research On the Critical Point Detectionmentioning

confidence: 99%

“…Generally, Bayesian inference, which is one of the most effective approaches, is conducted to estimate the parameters in this model. Specifically, the prior distributions are first placed over the model parameters; thereafter, the posterior distributions of the parameters can be estimated based on prior distributions and observation data [16]. However, posterior estimation is difficult to obtain if the model structure is nonlinear.…”

Section: A Three-phasementioning

confidence: 99%

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Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

Zhang

Shi

2017

Journal of Manufacturing Science and Engineering

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The quality of threaded pipe connections is one of the key quality characteristics of drill pipes, risers, and pipelines. This quality characteristic is evaluated mainly by a pair of critical points, which are corresponding to the mechanical deformations formed in the pipe connection process. However, these points are difficult to detect because of nonlinear patterns generated by latent process factors in torque signals, which conceal the true critical points. To address this problem, we propose a novel three-phase state-space model that incorporates physical interpretations of connection process to detect pairwise critical points. We also develop a two-stage recursive particle filter to estimate the locations of the underlying critical points. Results of a real threaded pipe connection case show that the detection performance of the proposed method is more powerful than that of other existing methods.

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“…The same rule in Ref. [16] is applied and p 1 ¼ p 2 ¼ 0.3 is chosen for transition probabilities to make a fair comparison. For simplicity, q is also chosen to be 0.3.…”

Section: Resultsmentioning

confidence: 99%

Section: Comparison With the Methods Frommentioning

confidence: 99%

Section: Review Of Related Research On the Critical Point Detectionmentioning

confidence: 99%

Section: A Three-phasementioning

confidence: 99%

See 2 more Smart Citations

Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

Zhang

Shi

2017

Journal of Manufacturing Science and Engineering

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“…During resampling, it is likely for low weighted particles to be removed. Thus, the diversity of the particles is reduced [32,[44][45][46][47][48]. For instance, if a small amount of particles of X t has the greatest weights, numerous resampled particles will end up being the same (there will be lesser distinct particles in X t ).…”

Section: Resamplingmentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

Hou

Chen

2016

Quality & Reliability Eng

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Online detection of whether a data stream has reached the steady state is known to be an important problem in many applications such as process control, data reconciliation and fault detection. This paper introduces a novel online steady state detection algorithm under the Bayesian framework based on a multiple change-point state space formulation and the sequential Monte Carlo methods. A Rao-Blackwellization technique is proposed to substantially reduce the variance of Monte Carlo estimation and greatly enhance the computational efficiency. In addition, a resampling scheme called the Optimal Resampling is used for eliminating duplicate samples and the robustness of steady state detection is significantly improved by using the information of the particles more efficiently. Numerical studies based on simulated signals and application to a real data set are used to evaluate the performance of the proposed method and compare with other existing methods from the literature. The proposed method is shown to establish a more robust performance than other methods. And it is much more computationally efficient than the standard sequential Monte Carlo method.where μ τt ;tÀ1 ; Σ τt ;tÀ1 ; a τt ;tÀ1 ; b τt ;tÀ1 are parameters associated with the line segment y τt :tÀ1 that can be obtained using equations in (11), t 1 (Á) the univariate non-standardized student's t-distribution.

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Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters

Cited by 32 publications

References 33 publications

Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

Adaptive memory-based single distribution resampling for particle filter

Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo

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