A Fuzzy Decision System for Fault Classification Under High Levels of Uncertainty

1995 ◽  
Vol 117 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Yubao Chen
Keyword(s):  
Fuzzy Inference ◽  
Information Gain ◽  
A Priori ◽  
Machining Process ◽  
Fault Classification ◽  
Membership Functions ◽  
Fuzzy Decision ◽  
Decision System ◽  
Practical Procedure ◽  
Multiple Sensors

The problem of high levels of uncertainty existing in machine diagnosis is addressed by an approach based on fuzzy logic. In this approach, multiple sensors/channels are used, and the uncertainty is treated by membership functions in different stages of the signal processing. The concepts of fuzziness, fuzzy set, and fuzzy inference are described, particularly for the development of a practical procedure for machine diagnosis. The membership functions are established through a learning process based on test data, rather than being selected a priori. The information-gain weighting functions are also introduced in order to improve the robustness and reliability of this method. As a result, a framework of a Fuzzy Decision System (FDS) is proposed and applied to a machining process. Experiment verification with an optimistic success rate of 97.5 percent was achieved.

scholarly journals An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7695
Author(s):  
Daniel Barry ◽  
Andreas Willig ◽  
Graeme Woodward
Keyword(s):  
Information Gain ◽  
Search Algorithm ◽  
A Priori ◽  
Search Space ◽  
Wireless Transmitters ◽  
Multiple Sensors ◽  
Single Target ◽  
Information Exploration ◽  
Speed Up ◽  
Priori Information

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

scholarly journals Type-II Fuzzy Decision Support System for Fertilizer

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ather Ashraf ◽  
Muhammad Akram ◽  
Mansoor Sarwar
Keyword(s):  
Decision Support ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Type I ◽  
Type Ii ◽  
Membership Functions ◽  
Fuzzy Decision ◽  
Fuzzy Decision Support System

Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm isO(mnr), wheremis the height of the raster,nis the width of the raster, andris the number of expert rules.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

STEREOVISION-BASED FUZZY OBSTACLE AVOIDANCE METHOD

2011 ◽  
Vol 08 (01) ◽  
pp. 169-183 ◽  
Author(s):  
LAZAROS NALPANTIDIS ◽  
ANTONIOS GASTERATOS
Keyword(s):  
Obstacle Avoidance ◽  
Efficient Solution ◽  
Fuzzy Inference ◽  
Autonomous Mobile Robots ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Inference System ◽  
Depth Maps ◽  
Complex Processes ◽  
Stereo Algorithm

This work presents a stereovision-based obstacle avoidance method for autonomous mobile robots. The decision about the direction on each movement step is based on a fuzzy inference system. The proposed method provides an efficient solution that uses a minimum of sensors and avoids computationally complex processes. The only sensor required is a stereo camera. First, a custom stereo algorithm provides reliable depth maps of the environment in frame rates suitable for a robot to move autonomously. Then, a fuzzy decision making algorithm analyzes the depth maps and deduces the most appropriate direction for the robot to avoid any existing obstacles. The proposed methodology has been tested on a variety of self-captured outdoor images and the results are presented and discussed.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

scholarly journals Economic Security Threat Modelling of a Commercial Bank in a Globalized Economy

2020 ◽  
pp. 142-151
Author(s):  
О.V. Dymchenko ◽  
О.О. Rudachenko ◽  
P. Gazzola
Keyword(s):  
Commercial Bank ◽  
Fuzzy Inference ◽  
A Priori ◽  
Economic Security ◽  
Security Threat ◽  
Research Information ◽  
Inference System ◽  
Security Research ◽  
Threat Modelling

In the paper, one develops a set of models for diagnosing threats to the economic security of a commercial bank, which allows improving the quality of decisions forming and making on managing the safe functioning and development of the bank. The bank's economic security research system has been developed, it includes 3 main blocks: research information space creation; assessment and analysis of the security of a commercial bank; generalization, and formation of decisions on the economic security of a commercial bank. The research made it possible to draw an inference of a theoretical, methodological, and applied nature that reflects the solution of the tasks set following the purpose of the study. A set of models has been built with modern tools of economic and mathematical modelling to improve the quality of decisions made to manage the bank's security and reduce the risks of threats. A model for calculating the bank's economic security indicator has been developed, which includes the following main stages: the construction of a structural scheme taking into account the rules of the theory of banking functioning security, then the terms and their membership functions are set for each input and output variable of the fuzzy inference system under consideration. Results of the response surface for the model are shown in the figure on the graphs of the dependence of the bank's economic security indicator on various input components. The paper requires that it is convenient to diagnose the state of economic security of a bank using fuzzy logic, this allows getting a clear quantitative representation of economic security state of the bank, as the indicators used for diagnostics may be indistinct and approximate and this a priori cannot give an adequate result when accurately calculated.

scholarly journals IASI-METOP and MIPAS-ENVISAT data fusion

2010 ◽  
Vol 10 (1) ◽  
pp. 183-211 ◽  
Author(s):  
S. Ceccherini ◽  
U. Cortesi ◽  
S. Del Bianco ◽  
P. Raspollini ◽  
B. Carli
Keyword(s):  
Data Fusion ◽  
Degrees Of Freedom ◽  
Solution Method ◽  
Information Gain ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Single Instrument ◽  
Fusion Products ◽  
Temporal Coverage

Abstract. The combination of data obtained with different sensors (data fusion) is a powerful technique that can provide target products of the best quality in terms of precision and accuracy, as well as spatial and temporal coverage and resolution. In this paper the results are presented of the data fusion of measurements of ozone vertical profile performed by two space-borne interferometers (IASI on METOP and MIPAS on ENVISAT) using the new measurement-space-solution method. With this method both the loss of information due to interpolation and the propagation of possible biases (caused by a priori information) are avoided. The data fusion products are characterized by means of retrieval errors, information gain, averaging kernels and number of degrees of freedom. The analysis is performed both on simulated and real measurements and the results demonstrate and quantify the improvement of data fusion products with respect to measurements of a single instrument.

Modeling Evolution of Microstructures Beneath Topographically Textured Surfaces Produced Using Shear Based Material Removal

2016 ◽  
Author(s):  
Saurabh Basu ◽  
Zhiyu Wang ◽  
Christopher Saldana
Keyword(s):  
Microstructure Evolution ◽  
Material Removal ◽  
A Priori ◽  
Orientation Imaging Microscopy ◽  
Machining Process ◽  
Textured Surfaces ◽  
Orientation Imaging ◽  
Using Data ◽  
Electron Back Scattered Diffraction ◽  
Tool Chatter

Tool chatter is envisaged as a technique to create undulations on fabricated biomedical components. Herein, a-priori designed topographies were fabricated using modulate assisted machining of oxygen free high conductivity copper. Subsequently, underpinnings of microstructure evolution in this machining process were characterized using electron back scattered diffraction based orientation imaging microscopy. These underpinnings were related to the unsteady mechanical states present during modulated assisted machining, this numerically modeled using data obtained from simpler machining configurations. In this manner, relationships between final microstructural states and the underlying mechanics were found. Finally, these results were discussed in the context of unsteady mechanics present during tool chatter, it was shown that statistically predictable microstructural outcomes result during tool chatter.

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

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