Extend stochastic petri nets-based method for modeling and representing process reliability of flexible manufacturing system
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Solution Overview
Problem
Conventional methods for modeling and representing process reliability of flexible manufacturing systems (FMS) fail to accurately evaluate reliability when real-time data changes during system operation, particularly due to the use of stochastic Petri nets assuming exponential distributions, which do not align with actual production data distributions.
Innovation Solution
An extend stochastic Petri nets (ESPN)-based method that performs modular division of FMS into process modules, constructs ESPN septuple models with arbitrary distribution transitions, and combines subnets to form a complete model for numerical simulation and reliability analysis, incorporating actual production parameters for real-time adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If stochastic Petri nets with exponential distribution are used for modeling FMS, then the modeling process is simplified, but the accuracy of reliability evaluation deteriorates due to mismatch with actual production data distributions
Solution Approach 1:
The patent changes the distribution parameter of transition times from exponential distribution to arbitrary distributions (normal, Poisson, uniform, triangular). This allows the model to match actual production data distributions while maintaining the SPN modeling framework, thus improving reliability evaluation accuracy without completely abandoning the original simplified approach
Solution Approach 2:
The patent introduces dynamic adjustment mechanisms where transition time distributions can be flexibly configured based on actual production characteristics. The model adapts to different production scenarios by selecting appropriate distribution types, making the reliability evaluation both accurate and adaptable to real-world variations
2Adaptability or versatility
If modular division of FMS is performed to improve model adaptability, then the model can adapt to real-time data changes, but the system complexity increases due to multiple subnets and their interactions
Solution Approach 1:
The patent divides the FMS into multiple process modules, each represented by a subnet with its own ESPN model. This segmentation allows independent modeling and analysis of each module while maintaining the ability to capture interactions between modules through token flow, thus achieving real-time adaptability without overwhelming complexity
Solution Approach 2:
The patent creates a universal subnet template that can be reused across different process modules. Each subnet follows the same ESPN structure with places, transitions, and arbitrary distributions, allowing the model to handle diverse production scenarios with a consistent framework, reducing overall model complexity through standardization
Data Source
AI summary
Disclosed is an extend stochastic Petri nets (ESPN)-based method for modeling and representing process reliability of a flexible manufacturing system (FMS), falling within the technical field of flexible manufacturing. Modular division is performed on an FMS, and performance parameters of various process modules are acquired; ESPN septuple models corresponding to subnets of Petri nets are constructed according to the performance parameters of the various process modules; based on a process flow, the subnets of Petri nets are combined according to a set control approach; and numerical values of transitions with arbitrary distributions in a complete ESPN model are solved through numerical simulation. In the disclosure, the accuracy for evaluating the process reliability of the FMS is ensured.