scholarly journals A novel multi-classifier information fusion based on Dempster–Shafer theory: application to vibration-based fault detection

2021 ◽  
pp. 147592172110071
Author(s):  
Vahid Yaghoubi ◽  
Liangliang Cheng ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Fault Detection ◽  
Turbine Blades ◽  
Classifier Fusion ◽  
Polycrystalline Nickel ◽  
Dempster Shafer Theory ◽  
Theory Application ◽  
The Individual ◽  
Shafer Theory ◽  
Preprocessing Technique ◽  
Classification Procedures

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this article, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster–Shafer theory. However, in cases with conflicting evidences, the Dempster–Shafer theory may give counterintuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Furthermore, it is applied for classifying polycrystalline nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that the proposed method improves the classification accuracy and outperforms the individual classifiers.

scholarly journals Multiple Criteria Group Decision-Making Method with Dempster–Shafer Theory and Probabilistic Linguistic Term Sets

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yuanwei Du ◽  
Susu Wang
Keyword(s):  
Decision Making ◽  
Group Decision ◽  
Individual Level ◽  
Preference Theory ◽  
Dempster Shafer Theory ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets ◽  
The Individual ◽  
Shafer Theory ◽  
Interval Valued

The motivation of this study is to propose a novel multiple criteria group decision-making (MCDGM) method based on Dempster–Shafer theory (DST) and probabilistic linguistic term sets (PLTSs) to handle the distinctions between compensatory information at the criterion level and noncompensatory information at the individual level in the process of information fusion. Initially, the information at the individual level is extracted by BPA functions. Then, they are fused with DST considering ignorance and DMs’ reliabilities. Next, the obtained BPA functions are transformed into interval-valued PLTSs with the assistance of intermediate belief and plausibility. Subsequently, the interval-valued PLTSs are converted into standard PLTSs. After normalization, the holistic PLTS is obtained with weighted addition operation and the round function is applied to determine the ultimate evaluation result. Finally, a case simulation study of evaluating the marine ranching ecological security is presented to verify and improve the validity and feasibility of the proposed method and algorithm in practical application. The proposed method and its relevant algorithm are both innovative combination of DST and PLTSs from the perspective of compensatory and noncompensatory features of information, which provides a new angle of view for the development of probabilistic preference theory and is beneficial to apply probabilistic preference theory in practice.

Finding Kicking Range of Sepak Takraw Game: Fuzzy Logic and Dempster-Shafer Theory Approach

2016 ◽  
Vol 2 (1) ◽  
pp. 187 ◽  
Author(s):  
Andino Maseleno ◽  
Md. Mahmud Hasan ◽  
Muhammad Muslihudin ◽  
Tri Susilowati
Keyword(s):  
Fuzzy Logic ◽  
Body Movement ◽  
Fuzzy Reasoning ◽  
The Other ◽  
Theory Approach ◽  
Dempster Shafer Theory ◽  
Novel Approach ◽  
The Individual ◽  
Shafer Theory ◽  
The Right

Sepak takraw is played by two regus, each consisting of three players. One of the three players shall be at the back and he is called a Tekong. The other two players shall be in front, one on the left and the other on the right. Having volley kicked a throw from the net by a team mate, the ball must then travel over the net to begin play. During the service, as soon as the Tekong kicks the ball, all the players are allowed to move about freely in their respective courts. The novel approach is the integration within a Tsukamoto's Fuzzy reasoning and inferences for evidential reasoning based on Dempster-Shafer theory. Sepak takraw is a highly complex net-barrier kicking sport that involves dazzling displays of quick reflexes, acrobatic twists, turns and swerves of the agile human body movement. Because of the human’s involvement in the game, the Fuzzy Logic type reasoning are the most appropriate. The individual rule outputs of Tsukamoto's Fuzzy reasoning scheme are crisp numbers, and therefore, the functional relationship between the input vector and the system output can be relatively easily identified. The result reveals that if  Tekong is kick far and front player is kick near then another regu's player is kick far, if  Tekong is kick near and front player is kick far then another regu's player is kick near, moreover possibility of kicking range is another regu's player is kick far in kicking range.<br /><br />

scholarly journals Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

2015 ◽  
Vol 773-774 ◽  
pp. 154-157 ◽  
Author(s):  
Muhammad Firdaus Rosli ◽  
Lim Meng Hee ◽  
M. Salman Leong
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Condition Monitoring ◽  
Support Vector ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Human Interpretation ◽  
The Individual ◽  
Shafer Theory ◽  
Automated Decision Making

Machines are the heart of most industries. By ensuring the health of machines, one could easily increase the company revenue and eliminates any safety threat related to machinery catastrophic failures. In condition monitoring (CM), questions often arise during decision making time whether the machine is still safe to run or not? Traditional CM approach depends heavily on human interpretation of results whereby decision is made solely based on the individual experience and knowledge about the machines. The advent of artificial intelligence (AI) and automated ways for decision making in CM provides a more objective and unbiased approach for CM industry and has become a topic of interest in the recent years. This paper reviews the techniques used for automated decision making in CM with emphasis given on Dempster-Shafer (D-S) evident theory and other basic probability assignment (BPA) techniques such as support vector machine (SVM) and etc.

scholarly journals Pipe burst diagnostics using evidence theory

2010 ◽  
Vol 13 (4) ◽  
pp. 596-608 ◽  
Author(s):  
Josef Bicik ◽  
Zoran Kapelan ◽  
Christos Makropoulos ◽  
Dragan A. Savić
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Information Sources ◽  
Water Distribution System ◽  
Evidence Theory ◽  
Dempster Shafer Theory ◽  
Pipe Burst ◽  
The Individual ◽  
Shafer Theory ◽  
Theory Of Evidence

This paper presents a decision support methodology aimed at assisting Water Distribution System (WDS) operators in the timely location of pipe bursts. This will enable them to react more systematically and promptly. The information gathered from various data sources to help locate where a pipe burst might have occurred is frequently conflicting and imperfect. The methodology developed in this paper deals effectively with such information sources. The raw data collected in the field is first processed by means of several models, namely the pipe burst prediction model, the hydraulic model and the customer contacts model. The Dempster–Shafer Theory of Evidence is then used to combine the outputs of these models with the aim of increasing the certainty of determining the location of a pipe burst within a WDS. This new methodology has been applied to several semi-real case studies. The results obtained demonstrate that the method shows potential for locating the area of a pipe burst by capturing the varying credibility of the individual models based on their historical performance.

scholarly journals Multi-Channel IoT-Based Ensemble-Features Fault Diagnosis for Machine Condition Monitoring

2020 ◽  
Author(s):  
Shang Gao ◽  
Cuicui Du
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Classifier Fusion ◽  
Machine Condition ◽  
Rolling Ball ◽  
Dempster Shafer Theory ◽  
Machine Condition Monitoring ◽  
Shafer Theory ◽  
Fusion Approach

This paper proposes a multi-channel internet of things (IoT)-based industrial wireless sensor network (IWSN) with ensemble-features fault diagnosis for machine condition monitoring and fault diagnosis. In this paper, the rolling bearing is taken as an example of monitored industrial equipment due to its wide use in industrial processes. The rolling bearing vibration signals are measured for further processing and analysis. On-sensor node ensemble feature extraction and fault diagnosis using Back Propagation network are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four rolling bearing operating in cage fracture, rolling ball spalling, inner ring spalling and outer ring spalling are monitored to evaluate the proposed system. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 94.21%, proving the feasibility of the proposed method to identify the bearing-fault patterns. This paper is conducted to provide new insights into how a high-accuracy IoT-based ensemble-features fault diagnosis algorithm is designed and further giving advisable reference to more IWSNs scenarios.

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