Shubin Si

Real-time information capturing and integration framework of the internet of manufacturing things

Currently, the typical challenges that manufacturing enterprises faced are the lack of timely, accurate and consistent information of manufacturing things (resources) during manufacturing execution. Real-time information visibility and traceability allows decision makers to make better-informed shop-floor decisions. In this article, a real-time information capturing and integration architecture of the internet of manufacturing things (IoMT) is presented to provide a new paradigm by extending the techniques of IoT to manufacturing field. Under this architecture and its key components, the manufacturing things such as operators, machines, pallets, materials etc. can be embedded with sensors, they can interact with each other. Considering the challenges of processing a huge amount of real-time data into useful information and exchange it among the heterogeneous application systems, a Real-time Manufacturing Information Integration Service (RTMIIS) has been designed to achieve seamless dual-way connectivity and interoperability among enterprise layer, workshop floor layer and machine layer. Finally, a near-life scenario has been used to illustrate the proof-of-concept application of the proposed IoMT.

Integrated Importance Measure of Component States Based on Loss of System Performance

This paper mainly focuses on the integrated importance measure (IIM) of component states based on loss of system performance. To describe the impact of each component state, we first introduce the performance function of the multi-state system. Then, we present the definition of IIM of component states. We demonstrate its corresponding physical meaning, and then analyze the relationships between IIM and Griffith importance, Wu importance, and Natvig importance. Secondly, we present the evaluation method of IIM for multi-state systems. Thirdly, the characteristics of IIM of component states are discussed. Finally, we demonstrate a numerical example, and an application to an offshore oil and gas production system for IIM to verify the proposed method. The results show that 1) the IIM of component states concerns not only the probability distributions and transition intensities of the states of the object component, but also the change in the system performance under the change of the state distribution of the object component; and 2) IIM can be used to identify the key state of a component that affects the system performance most.

Theory of constraints product mix optimisation based on immune algorithm

Product mix optimisation is one of the most fundamental problems in manufacturing enterprise. As an important component in theory of constraints (TOC), product mix optimisation is solved by the TOC heuristic (TOCh) and some intelligent search algorithms, even though these approaches often cannot effectively obtain a good solution in the previous attempts, especially for the large-scale product mix optimisation. Aiming at this problem, a contribution has been made to the following aspects in the present paper. Firstly, a model of TOC product mix optimisation, which identifies and exploits the capacity constrained resource (CCR) to maximise system throughput is put forward and simplified by cutting down some constraints of non-CCRs. Secondly, an intelligent optimisation approach based on immune algorithm (IA) and TOC for product mix optimisation is presented to search optimal solution(s), whether it is a small-scale or large-scale instance. Thirdly, the immune mechanisms, such as the immune response mechanism, immune self-adaptive regulation and vaccination, are studied in detail, which not only greatly improves the searching ability and adaptability, but also evidently increases the global convergence rate of immune evolution. Fourthly, the proposed approach is implemented and applied in both small-scale and large-scale product mix optimisation. Finally, a comparison between the proposed approach and existing approaches is made. Simulation results show that the proposed approach is superior to the existing approaches, such as the TOCh, revised TOCh, integer linear programming (ILP), tabu search (TS), and genetic algorithms (GA).

Semi-Markov Process-Based Integrated Importance Measure for Multi-State Systems

Importance measures in reliability engineering are used to identify weak components of a system and signify the roles of components in contributing to proper functioning of the system. Recently, an integrated importance measure (IIM) has been proposed to evaluate how the transition of component states affects the system performance based on the probability distributions and transition rates of component states. In the system operation phase, the bathtub curve presents the change of the transition rate of component states with time, which can be described by three different Weibull distributions. The behavior of a system under such distributions can be modeled by the semi-Markov process. So, based on the reported IIM equations of component states, this paper studies how the transition of component states affects system performance under the semi-Markov process. This measure can provide useful information for preventive actions (such as monitoring enhancement, construction improvement, etc.), and provide support to improve system performance. Finally, a simple numerical example is presented to illustrate the utilization of the proposed method.

Identifying product failure rate based on a conditional Bayesian network classifier

Research highlights? CBN introduces the conditional independence relationships among attribute variables. ? CBN provides an effective approach to classify the failure rate rank of products. ? CBN increases the classification accuracy. ? CBN makes an acceptable balance between classifier complexity and performance. To identify the product failure rate grade under diverse configuration and operation conditions, a new conditional Bayesian networks (CBN) model is brought forward. By indicating the conditional independence relationship between attribute variables given the target variable, this model could provide an effective approach to classify the grade of failure rate. Furthermore, on the basis of the CBN model, the procedure of building product failure rate grade classifier is elaborated with modeling and application. At last, a case study is carried out and the results show that, with comparison to other Bayesian networks classifiers and traditional decision tree C4.5, the CBN model not only increases the total classification accuracy, but also reduces the complexity of network structure.

Component state-based integrated importance measure for multi-state systems

Importance measures in reliability engineering are used to identify weak components and/or states in contributing to the reliable functioning of a system. Traditionally, importance measures do not consider the possible effect of groups of transition rates among different component states, which, however, has great effect on the component probability distribution and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) to estimate the effect of a component residing at certain states on the performance of the entire multi-state systems. This generalization of IIM describes in which state it is most worthy to keep the component to provide the desired level of system performance, and which component is the most important to keep in some state and above for improving the performance of the system. An application to an oil transportation system is presented to illustrate the use of the suggested importance measure.

Competitiveness of Chinese high‐tech manufacturing companies in global context

– The purpose of this paper is to study the operational competitiveness and identify the development route of Chinese high‐tech manufacturing companies by comparing with other similar international manufacturing companies of global manufacturing strategies database., – The preliminary analytical models for competitiveness analysis are used to analyze the operational competitiveness strategies in three different types of Chinese high‐tech manufacturing companies based on the weights of the multi‐criteria manufacturing strategies, which are calculated using analytic hierarchy process method. Benchmarking between case companies and leading companies of prospector, analyzer and defender groups is applied to evaluate the manufacturing strategies further., – As a result of the case studies, it is possible to understand operational competitiveness manufacturing strategies for the case companies, to show one development route for Chinese high‐tech manufacturing companies to be competitive in their markets., – Chinese high‐tech manufacturing companies have their own operational strategies in different development phase. The different weights of important factors such as quality, cost, time and flexibility make the case companies to have some advantages in prospector, analyzer and defender. The preliminary analytical models are effective for Chinese high‐tech manufacturing companies to calculate their operational competitiveness under the influence of Chinese culture and macro‐control., – Benchmarking of operational competitiveness is presented to evaluate the manufacturing strategies in this paper. One development route of Chinese high‐tech manufacturing companies, which is under the influence of Chinese culture and macro‐control, is promoted.

The Integrated Importance Measure of Multi-State Coherent Systems for Maintenance Processes

This paper mainly focuses on the integrated importance measure (IIM) of component states for maintenance processes. To describe the impact of each component state in maintenance processes, a maintenance cost function of multi-state systems is defined at first. Second, considering the probability distributions, transition rates of the component states, and system maintenance costs, the IIM of component states is described. The corresponding characteristics of the IIM of the component states are discussed in both series systems and parallel systems. Then the relationships between IIM and Griffith importance, Wu importance, mean absolute deviation, and multi-state redundancy importance measures are also discussed. At last, a numerical example is given to demonstrate the IIM of component states. The results show that IIM can be used to identify the most important component state for the maintenance decision.

Component Importance for Multi-State System Lifetimes With Renewal Functions

Importance measures are widely used to characterize the roles of components in systems. The system lifetime can be divided into different life stages. Traditionally, importance measures do not consider the possible effect of the expected number of component failures over a systems lifetime and over different life stages, which, however, has a great effect on the system performance changes, and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) from unit time to system lifetime, and to different life stages. Based on the renewal functions of components, this measure can evaluate the changes of the system performance due to component failures. This generalization of the IIM describes which component is the most important to improve the performance of the system during the system lifetime and at different life stages. An example of the application of an oil transportation system is presented to illustrate the use of the generalized IIM.

A novel decision diagrams extension method

Abstract Binary decision diagram (BDD) is a graph-based representation of Boolean functions. It is a directed acyclic graph (DAG) based on Shannon׳s decomposition. Multi-state multi-valued decision diagram (MMDD) is a natural extension of BDD for the symbolic representation and manipulation of the multi-valued logic functions. This paper proposes a decision diagram extension method based on original BDD/MMDD while the scale of a reliability system is extended. Following a discussion of decomposition and physical meaning of BDD and MMDD, the modeling method of BDD/MMDD based on original BDD/MMDD is introduced. Three case studies are implemented to demonstrate the presented methods. Compared with traditional BDD and MMDD generation methods, the decision diagrams extension method is more computationally efficient as shown through the running time.