Energy Modeling and Simulation of Flexible Manufacturing Systems Based on Colored Timed Petri Nets

Wang, Qiulian; Wang, Xianglian; Yang, Suihong

doi:10.1111/jiec.12180

Cited by 14 publications

(4 citation statements)

References 29 publications

(26 reference statements)

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“…For instance, Abele et al leveraged the process plans from Programmable Logic Controllers (PLCs) to collect energy-relevant data of production machines [17]; Shang et al modeled the variable power flows of the heavy-duty machine tool and use the data of machine parameters such as spindle speed, feed speed, axes position from the NC system for power consumption analysis [18]. (3) Online monitoring: Physics-based experiments and empirical models are often offline and tend to be limited in the ability for real-time analysis of energy efficiency [19].…”

Section: Research Backgroundmentioning

confidence: 99%

Sensor-Based Recurrence Analysis of Energy Efficiency in Machining Processes

Wang

Yang

2020

IEEE Access

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Manufacturing industry incurs a large portion of energy consumption and carbon emission in the economy. Traditionally, smart energy management depends on aggregated measures from billing information, as well as physics-based models, empirical results derived from extensive experiments, which tend to be limited in the ability for real-time monitoring of energy efficiency. There is an urgent need to develop energy monitoring solutions for more transparency about energy use. This paper presents a new sensor-based approach for recurrence analysis of continuous power signals and multi-state modeling of energy efficiency in the machining process. First, we leverage the recurrence plot to characterize the nonlinear variations in power signals and further help delineate different states in the machining process, thereby providing statistics of energy consumption in each state. Second, we compute the composite index of energy efficiency for each workpiece and then develop multivariate statistical control charts for process monitoring of continuous production of workpieces. Third, after an anomaly is detected, we propose the orthogonal decomposition approach to diagnose the root cause of abnormal states in the energy use. The proposed methodology is evaluated and validated on real-world manufacturing of shaft-like parts in a machine shop. Experimental results show that the prediction error of energy efficiency from sensorbased models is within 5% from the ground truth, which show great potentials to implement sensor-based monitoring and analysis of real-time energy efficiency in the manufacturing process. INDEX TERMS Energy efficiency, process monitoring, recurrence analysis, root cause diagnosis, manufacturing process, sustainable manufacturing.

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Section: Research Backgroundmentioning

confidence: 99%

Sensor-Based Recurrence Analysis of Energy Efficiency in Machining Processes

Wang

Yang

2020

IEEE Access

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“…Pang and Le [32] built a weighted p-timed Petri net model to schedule a flexible manufacturing system and generate near-optimal energy schedules. For evaluating energy consumption, Wang et al [33] presented an energy consumption model for machining systems based on colored timed Petri nets, where the uncertainty of task assignment and the volatility of operation time were treated by Petri net functions. Li et al [34] used a colored timed object-oriented Petri net to model and predict hybrid energy behaviors of flexible machining systems.…”

Section: Related Workmentioning

confidence: 99%

Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

et al. 2019

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The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things technology, which makes it possible to sense and collect real-time data from the production line and provide more opportunities for online control for energy saving purposes. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to decide the machine energy saving state considering the production information of a discrete stochastic manufacturing system. Fuzzy knowledge for energy saving operations of a machine is represented in weighted fuzzy production rules with certain values. The rules describe uncertain, imprecise, and ambiguous knowledge of machine state decisions. This makes an energy saving sleep decision in advance when a machine has the inclination of starvation or blockage, which is based on the real-time production rates and level of connected buffers. A dynamic adaptive fuzzy reasoning Petri net is formally defined to implement the reasoning process of the machine state decision. A manufacturing system case is used to demonstrate the application of our method and the results indicate its effectiveness for energy saving operation purposes.

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“…Simulation models are proposed to address these dynamics and support the design and control of manufacturing systems (Duflou et al ). Wang and colleagues () look at the energy consumption of flexible manufacturing systems and investigate the main influencing factors, such as uncertainty of task assignment and changing operation times. An energy model based on colored timed petri nets is developed, allowing simulation experiments and to analyze the energy consumption in detail.…”

Section: …To Manufacturing and Remanufacturingmentioning

confidence: 99%