Emergence: From Chaos to Order by John H. Holland .

As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing toits advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information.Uncertainty measure plays an essential role both in evidence theory and probability theory.In probability theory, Shannon entropy provides a novel perspective for measuring uncertainty. Various entropies exist for measuring the uncertainty of basic probability assignment (BPA) in evidence theory. However, from the standpoint of the requirements of uncertainty measurement and physics, these entropies are controversial. Therefore, the process for measuring BPA uncertainty currently remains an open issue in the literature.Firstly, this paper reviews the measures of uncertainty in evidence theory followed by an analysis of some related controversies. Secondly, we discuss the development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures. We also examine the concept of maximum Deng entropy, the pseudo-Pascal triangle of maximum Deng entropy, generalized belief entropy, and measures of divergence.In addition, we conduct an analysis of the application of Deng entropy and further examine the challenges for future studies on uncertainty measurement in evidence theory. Finally, a conclusion is provided to summarize this study.

Uncertainty measure in evidence theory

Failure mode and effect analysis using multi-criteria decision making methods: A systematic literature review

Failure mode and effect analysis (FMEA) is an effective risk-management tool, which has been extensively utilized to manage failure modes (FMs) of products, processes, systems, and services. Almost all FMEA models are concerned with how to get a complete risk order of FMs from highest to lowest risk. However, in many situations, it may be sufficient to classify the FMs into several ordinal risk classes. Meanwhile, generating a consensual decision is crucial for the FMEA problem because 1) reaching consensus will enhance the connections among FMEA participants, and 2) a highly accepted group solution to the FMEA problem can be generated. Thus, this study proposes a consensus-based group decision-making framework for FMEA with the aim of classifying FMs into several ordinal risk classes in which we assumed that FMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information. In the FMEA framework, a consensus-driven methodology is presented to generate the weights of risk factors. Following this, an optimization-based consensus rule guided by a minimum adjustment distance policy is devised, and an interactive model for reaching consensus is developed to generate consensual FM risk classes. In order to justify its validity of the proposal, our framework is applied for the risk evaluation of proton beam radiotherapy.

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Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach

Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

Failure mode and effects analysis (FMEA) is a widely used reliability analysis tool for identifying and eliminating known or potential failures in system, design, and process. In traditional FMEA, failure modes are evaluated by FMEA team members with respect to three risk factors: severity (S), occurrence (O), and detectability (D), and ranked via their risk priority number (RPN), which is obtained by multiplying the crisp values of S, O, and D. However, traditional RPN has been considerably criticized due to the following shortcomings: not considering the different weights of risk factors; the identical value of RPN for different combinations of S, O, and D; the diversity and uncertainty of evaluation information given by FMEA team members and without considering the dependence among different failure modes. Although significant efforts have been made in FMEA literatures to overcome these shortcomings, there are still some deficiencies. In this paper, a new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach. In the proposed model, the HoR is introduced to identify the dependence among different failure modes and the link between failure modes and the risk factors of O, D and the subcriteria of S. Rough number is introduced to manipulate the subjectivity and vagueness in decision making and VIKOR approach is used to determine the risk priority order of failure modes in a comprehensive way. Finally, an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.

Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach

Performance pattern identification is the key basis for fault detection and condition prediction, which plays a major role in ensuring safety and reliability in complex electromechanical systems (CESs). However, there are a few problems related to the automatic and adaptive updating of an identification model. Aiming to solve the problem of identification model updating, a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns. First, an unsupervised clustering technique is used to construct an initial identification model. Second, the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response. Third, incremental learning is employed to trace the dynamic changes of patterns, and operations such as pattern insertion, pattern removal, and pattern revision are designed to realize automatic and adaptive updates of an identification model. The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications. As an unsupervised and self-adapting approach, the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes. Therefore, this method can be flexibly and efficiently used for performance pattern identification of the CESs. Moreover, the proposed method provides a foundation for fault detection and condition prediction, and can be used in other engineering applications.

An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems

Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: Application to the gas insulated metal enclosed transmission line (GIL)

The optimisation of resource allocation is an important target of the manufacturing enterprise in continuous business process improvement. It is a suitable method that helps enterprise to discover irrationally allocated resources through analysis of the original resource allocation, and then optimise the resource allocation of the business process gradually. To identify the irrationally allocated resources, a method to analyse the original resource allocation in business process based on network centrality analysis is introduced. The main emphasis of this article is focused on the relationships among the resources. And the influence of resources is used to reflect the result of resource allocation. First, on the basis of the relationships by handover of business tasks, the dependent network among the resources is established using the event logs of the business process. Then, the eigenvector centrality-based algorithm is provided to calculate the influence of resources. In addition, the balance of the resources' influence is defined as the index of the optimisation of resource allocation. And the irrationally allocated resources are found out by Grubbs' criterion. Finally, an example to identify irrationally allocated resources of quality control process in a manufacturing enterprise is reported to verify the proposed approach.

The identification of irrationally allocated resources in business process based on network centrality analysis

Multifractal analysis is applied to extract nonlinear features from complex systems for condition recognition. Abnormal condition is hazardous for process industry complex system which may lead to accidents. Comparing with traditional techniques of condition recognition without concerning nonlinearity of complex system, multifractal spectrum elaborately reveals scale-invariance or self-similarity pro perties of observed data, which is one of the intrinsic characteristics of complex system. By using Multifractal Detrended Fluctuation Analysis (MF-DFA) algorithm, multifractal spectrum is calculated directly from monitoring time series data. The shape of multifractal spectrum is used to distinguish abnormal conditions from normal ones of complex system. After multi-source information fusion based on Dempster-Shafer evidence theory, the proposed approach can be used for abnormal condition recognition in process industry com plex system where continuous multi-channel data are monitored. The effectiveness of the approach is illustrated using data from a simulated dataset and a chemical plant model where potential abnormal conditions are detected effectively, thus avoid severe system safety problems.

Kun Chen

Papers

Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach

An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems

Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: Application to the gas insulated metal enclosed transmission line (GIL)

The identification of irrationally allocated resources in business process based on network centrality analysis

Condition recognition of complex systems based on multi-fractal analysis