Particle filtering without tears: A primer for beginners

Tulsyan, Aditya; Gopaluni, R. Bhushan; Khare, Swanand

doi:10.1016/j.compchemeng.2016.08.015

Cited by 36 publications

(19 citation statements)

References 18 publications

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“…For the sake of brevity, we assume that the readers are familiar with the theory of particle methods. For a detailed exposition on particle methods the reader is referred to a recently published tutorial on this subject or Section 6 in Ref. .…”

Section: Particle Methodsmentioning

confidence: 99%

Design and assessment of delay timer alarm systems for nonlinear chemical processes

2017

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In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this article, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time-series models. The alarm design for such complex processes faces three important challenges: (1) industrial processes exhibit highly nonlinear behavior;(2) state variables are not precisely known (modeling error); and (3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this article, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates-two common performance measures for alarm systems. To ensure the alarm design is robust to any non-stationary process behavior, an expected-case and a worst-case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non-stationary chemical reactor problem. V C 2017 American Institute of Chemical Engineers AIChE J, 64: 77-90, 2018

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Section: Particle Methodsmentioning

confidence: 99%

Design and assessment of delay timer alarm systems for nonlinear chemical processes

2017

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“…We used a Bayesian inference approach (Tulsyan et al, 2016) through the VENSIM's built-in Markov Chain Monte Carlo & Simulated Annealing method to sample the posterior distributions of these parameters and estimate the most likely vector of values for all of them (Rahmandad et al, 2015). Supplementary Appendix A3 displays the convergence metrics.…”

Section: Model Calibrationmentioning

confidence: 99%

A Landscape-Level Assessment of Restoration Resource Allocation for the Eastern Monarch Butterfly

Solis-Sosa

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Larrivée

et al. 2021

Front. Environ. Sci.

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The Monarch butterfly eastern population (Danaus plexippus) is in decline primarily due to habitat loss. Current habitat restoration programs focus on re-establishing milkweed, the primary food resource for Monarch caterpillars, in the central United States of America. However, individual components of the Monarch life cycle function as part of an integrated whole. Here we develop the MOBU-SDyM, a migration-wide systems dynamics model of the Monarch butterfly migratory cycle to explore alternative management strategies’ impacts. Our model offers several advances over previous efforts, considering complex variables such as dynamic temperature-dependent developmental times, dynamic habitat availability, and weather-related mortality across the entire range. We first explored whether the predominant focus of milkweed restoration in the mid-range of the Monarch’s migration could be overestimating the Monarch’s actual habitat requirements. Second, we examined the robustness of using the recommended 1.2–1.6 billion milkweed stems as a policy objective when accounting for factors such as droughts, changes in temperature, and the stems’ effective usability by the Monarchs. Third, we used the model to estimate the number and distribution of stems across the northern, central, and southern regions of the breeding range needed to reach a self-sustainable long-term Monarch population of six overwintering hectares. Our analysis revealed that concentrating milkweed growth in the central region increases the size of the overwintering colonies more so than equivalent growth in the south region, with growth in the northern region having a negligible effect. However, even though simulating an increase in milkweed stems in the south did not play a key role in increasing the size of the overwintering colonies, it plays a paramount role in keeping the population above a critically small size. Abiotic factors considerably influenced the actual number of stems needed, but, in general, our estimates of required stems were 43–91% larger than the number of stems currently set as a restoration target: our optimal allocation efforts were 7.35, 92, and 0.15% to the south, central, and northern regions, respectively. Systems dynamics’ analytical and computational strengths provided us with new avenues to investigate the Monarch’s migration as a complex biological system and to contribute to more robust restoration policies for this unique species.

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“…The key element of the autonomous health management process, RUL prediction, can be performed based on various data-driven techniques (Si et al 2011), from statistics to machine learning. Among these techniques, Particle Filtering is an efficient method that is widely used to accomplish the RUL prediction task (Tulsyan et al 2016;Wang et al 2019). This method allows building the degradation model of the monitored system and then, based on this result, forecasting its RUL.…”

Section: Maintenance Decision-makingmentioning

confidence: 99%

RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques

et al. 2020

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This work aims to provide useful insights into the course of action and the challenges faced by machine manufacturers when dealing with the actual application of Prognostics and Health Management procedures in industrial environments. Taking into account the computing capabilities and connectivity of the hardware available for smart manufacturing, we propose a particular solution that allows meeting one of the essential requirements of intelligent production processes, i.e., autonomous health management. Indeed, efficient and fast algorithms, that does not require a high computational cost and can be appropriately performed on machine controllers, i.e., on edge, are combined with others, which can handle large amounts of data and calculations, executed on remote powerful supervisory platforms, i.e., on the cloud. In detail, new condition monitoring algorithms based on Model-of-Signals techniques are developed and implemented on local controllers to process the raw sensor readings and extract meaningful and compact features, according to System Identification rules and guidelines. These results are then transmitted to remote supervisors, where Particle Filters are exploited to model components degradation and predict their Remaining Useful Life. Practitioners can use this information to optimise production planning and maintenance policies. The proposed architecture allows keeping the communication traffic between edge and cloud in the nowadays affordable “Big data” range, preventing the unmanageable “Huge data” scenario that would follow from the transmission of raw sensor data. Furthermore, the robustness and effectiveness of the proposed method are tested considering a meaningful benchmark, the PRONOSTIA dataset, allowing reproducibility and comparison with other approaches.

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Particle filtering without tears: A primer for beginners

Cited by 36 publications

References 18 publications

Design and assessment of delay timer alarm systems for nonlinear chemical processes

Design and assessment of delay timer alarm systems for nonlinear chemical processes

A Landscape-Level Assessment of Restoration Resource Allocation for the Eastern Monarch Butterfly

RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques

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