Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems

Chen, Wenchong; Liu, Hongwei; Qi, Ershi

doi:10.1016/j.jmsy.2020.03.002

Cited by 42 publications

(12 citation statements)

References 40 publications

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“…From the MDR results obtained from the statistics T 2 and Q with PCA for the fixed-time approach (2nd column of Tables 2 and 3), the set of twenty-one faults of the Tennessee benchmark problem can be grouped into four subsets according to the level of detection difficulty. Group 1 refers to easy faults (1,2,4,6,7,8,12,13,14); group 2, to intermediate faults (11,17,18); group 3, to hard faults (5,10,16,19,20,21); and group 4, to very hard faults (3,9,15). First, significant MDR gains were verified for eight faults, all related to the Q statistic.…”

Section: Resultsmentioning

confidence: 98%

“…The authors reported that this data filtering facilitated the identification of energy consumption patterns of devices by vector support machines. [14] designed an event-based model predictive control strategy to more efficiently reconfigure production logic in real-time. An industrial sewing machine production plant was used as a case study, focusing on smart factories.…”

Section: Introductionmentioning

confidence: 99%

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Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

et al. 2022

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Data acquisition in process industries usually takes place at each sampling. The disadvantage is that a considerable amount of data without new information about the state of the process is continuously transmitted and processed. This negatively affects the communication system and computational power, which is more critical nowadays given the number of variables measured, even in seconds. One solution concerns the event-driven paradigm, in which only relevant data according to a pre-defined criterion is forwarded for further processing. This work investigated the event-based threshold and delta methods in the context of fault detection. The data transmission rate was also analyzed. The well-know Tennessee Eastman problem (TEP) was used as a case study. The fault detection system was based on PCA (principal component analysis), which is widely used for this purpose in this benchmark. The results were compared with the commonly used time-based approach, for a fixed false alarm rate. The threshold rule provided similar results, but with much less data. For the delta rule, significant MDR (missed detection rate) gains of up to 74% were obtained for five of the six hard-to-detect faults, and of up to 69%, for two of the three very hard-to-detect faults. MDR values very close to zero were reached for two of the three intermediate detection faults and two of the hard-to-detect faults. The detection time was also evaluated. In this regard, considerably lower values were obtained for all intermediate detection faults, three of the hard-to-detect faults and all very hard-to-detect faults. In short, the delta method was able to improve fault detection performance, especially for hard-to-detect faults, with a considerably lower data transmission rate, around 20% on average. Event-driven data acquisition can be very attractive for process industries.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

et al. 2022

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“…A numerical approach has been applied to design the buffer in an automated transfer line to alleviate the effect of breakdown on the line efficiency [15]. Several studies utilized simulation techniques to deal with SPL optimization problem [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Development of an Efficient Prediction Model for Optimal Design of Serial Production Lines

et al. 2021

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One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools' characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

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“…Modeling with event systems enjoys growing popularity because of their ability to adequately represent the world around us, especially in fields of business processes [1], production systems [2], networked control systems [3], etc. Therefore, a software for system modeling is often used to analyze system behavior.…”

Section: Introductionmentioning

confidence: 99%

Robotic Sorting Line Model Using Coloured Petri Net

Štursa

Havlicek

Kupka

2021

Software Engineering and Algorithms

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With the increasing availability of robots and image processing systems, the automated robotic line implementations are still extensively increasing. As well, the complexity of these systems increases, which increases the demands on the determination of their function and possible analysis. For these purposes the complex robotic systems are modeled. Here, the Coloured Petri Nets were used to model the robotic sorting line. Specifically, the CPN Tools software was used to model creation, simulation, analysis and to made experiments. State space analysis was done to revelation of possible unwanted deadlocks. Furthermore, the sorting line model was used to found out the maximum number of objects that can be sorted in a defined time. As the robot manipulation time directly affects maximum number of objects in one batch, the timing procedure was created to declare robot operation dependency on types of manipulated object. The state space analysis showed that there are no unwanted deadlocks in the system. The model has been verified and declared as correct. Optimal composition and order of objects was successfully found by timed coloured Petri nets experiments.

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Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems

Cited by 42 publications

References 40 publications

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

Development of an Efficient Prediction Model for Optimal Design of Serial Production Lines

Robotic Sorting Line Model Using Coloured Petri Net

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