scholarly journals Fault Model for UML Behavioral Activity and Sequence Diagrams

2019 ◽  
Vol 9 (1) ◽  
pp. 6926-6929
Keyword(s):  
Fault Detection ◽  
Software Testing ◽  
Fault Model ◽  
Behavioral Activity ◽  
Sequence Diagrams ◽  
Coverage Criteria ◽  
Testing Methodology ◽  
Different Types ◽  
Uml Diagrams ◽  
Types Of Faults

In software testing, the fault detection in any software construct is very important factor to check how efficiently testing process is carried out. While testing software, it is required to take some coverage criteria to check the testing methodology. The paper shows a way for fault detection for UML behavioral diagrams. Different types of faults which can occur in UML diagrams are discussed and a fault model is proposed for combinational diagram made by integrating UML behavioral diagram such as activity and sequence diagrams. The percentage of fault detected in software is calculated using fault model and to prove how efficient is the software testing process.

Case Study Investigation of the Fault Detection and Error Locating Effects of Architecture-based Software Testing

2020 ◽  
Vol 30 (02) ◽  
pp. 191-216
Author(s):  
Jihyun Lee ◽  
Sungwon Kang
Keyword(s):  
Fault Detection ◽  
Software Testing ◽  
Case Studies ◽  
Detection Rate ◽  
Software System ◽  
Contributing Factors ◽  
Types Of Faults ◽  
Test Architecture

For software testing, it is well known that the architecture of a software system can be utilized to enhance testability, fault detection and error locating. However, how much and what effects architecture-based software testing has on software testing have been rarely studied. Thus, this paper undertakes case study investigation of the effects of architecture-based software testing specifically with respect to fault detection and error locating. Through comparing the outcomes with the conventional testing approaches that are not based on test architectures, we confirm the effectiveness of architecture-based software testing with respect to fault detection and error locating. The case studies show that using test architecture can improve fault detection rate by 44.1%–88.5% and reduce error locating time by 3%–65.2%, compared to the conventional testing that does not rely on test architecture. With regard to error locating, the scope of relevant components or statements was narrowed by leveraging test architecture for approximately 77% of the detected faults. We also show that architecture-based testing could provide a means of defining an exact oracle or oracles with range values. This study shows by way of case studies the extent to which architecture-based software testing can facilitate detecting certain types of faults and locating the errors that cause such faults. In addition, we discuss the contributing factors of architecture-based software testing which enable such enhancement in fault detection and error locating.

scholarly journals Synthesis of Test Scenarios Using UML Sequence Diagrams

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Ashalatha Nayak ◽  
Debasis Samanta
Keyword(s):  
Sequence Diagrams ◽  
System Behavior ◽  
Test Scenario ◽  
Coverage Criteria ◽  
Different Types ◽  
Levels Of Automation ◽  
Uml 2.0 ◽  
Test Scenarios ◽  
Arbitrary Flow ◽  
Testable Model

UML 2.0 sequence diagrams are used to synthesize test scenarios. A UML 2.0 sequence diagram usually consists of a large number of different types of fragments and possibly with nesting. As a consequence, arriving at a comprehensive system behavior in the presence of multiple, nested fragment is a complex and challenging task. So far the test scenario synthesis from sequence diagrams is concerned, the major problem is to extract an arbitrary flow of control. In this regard, an approach is presented here to facilitate a simple representation of flow of controls and its subsequent use in the test scenario synthesis. Also, the flow of controls is simplified on the basis of UML 2.0 control primitives and brought to a testable form known as intermediate testable model (ITM). The proposed approach leads to the systematic interpretation of control flows and helps to generate test scenarios satisfying a set of coverage criteria. Moreover, the ability to support UML 2.0 models leads to increased levels of automation than the existing approaches.

scholarly journals Fault detection technique for test cases in software engineering

2018 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Jagatjot Singh ◽  
Sumit Sharma
Keyword(s):  
Software Engineering ◽  
Fault Detection ◽  
Back Propagation ◽  
Test Case ◽  
Test Cases ◽  
Engineering Process ◽  
Different Types ◽  
Software Engineering Process ◽  
Propagation Technique ◽  
Types Of Faults

The processing of software and performing various operations on it is known as a software engineering process. The application of test cases for detecting the faults within the software is done through the testing process. There are various types of faults that occur within a software or test case which are to be identified and preventive approaches are to be applied to prevent them. In this paper, the Learn-to-rank algorithm is utilized which helps in detecting the faults from the software. The Back-Propagation technique is included with the LRA approach for enhancing its performance and improving the detection of fault rate. 10 test cases of different types are used for running various experiments and the MATLAB tool is utilized for performing various simulations. It is seen through the various simulation results that the fault detection rate is increased as well as the execution time is minimized with the help of this approach. 

Modal Current and Cumulative Sum Based Fault Detection Approach in Transmission Lines

2018 ◽  
Vol 19 (6) ◽  
Author(s):  
Mohammad Amin Jarrahi ◽  
Haidar Samet
Keyword(s):  
Fault Detection ◽  
Transmission Lines ◽  
Fault Analysis ◽  
Cumulative Sum ◽  
Efficiency Assessment ◽  
Fast Approach ◽  
Detection Approach ◽  
Three Phase ◽  
Different Types ◽  
Types Of Faults

AbstractIn this paper, a simple and fast approach is suggested for fault detection in transmission lines. The proposed technique utilizes a modified cumulative sum approach for a modal current to identify faults. The modal current is derived by proper linear mixing of three-phase currents. Since different types of faults may occur in transmission lines, all three-phase currents should be considered during fault analysis. By converting three-phase currents to a modal current, the processing time is reduced and less memory is needed. In this paper, a modal current is processed instead of three-phase currents. The modified cumulative sum approach presented in this paper is capable of decreasing computational burdens on the digital relay and accelerating the fault detection procedure. The proposed fault detection technique is evaluated in four different systems. Moreover, some real recorded field data were deliberated in the efficiency assessment of the proposed method. The results denote high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods from different aspects.

scholarly journals Fault Detection and Diagnosis in Multi-Robot Systems: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4019 ◽  
Author(s):  
Eliahu Khalastchi ◽  
Meir Kalech
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Research Opportunities ◽  
The World ◽  
Different Types ◽  
Robot Systems ◽  
Detection And Diagnosis ◽  
Types Of Faults ◽  
Multi Robot

The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS.

scholarly journals PLC AND HMI FOR CONVEYOR MONITORING AND FAULT DETECTION

2021 ◽  
Vol 9 (5) ◽  
pp. 34-38
Author(s):  
Mrunal Deshkar ◽  
◽  
Dipanjali Padhi ◽  
Keyword(s):  
Fault Detection ◽  
Fire Safety ◽  
Programmable Logic Controller ◽  
New Method ◽  
Programmable Logic ◽  
Different Types ◽  
Types Of Faults

The conveyor is usually used in industries to transport the commodity from one end to another, and can use it anywhere. As the occurrence of faults can affect the entire generation of power, the monitoring and security of these conveyors is important. Using relay logic methods, which have many drawbacks, the safety of the conveyors is carried out, and a new method is therefore required. This paper focuses on the monitoring, control, and safety of conveyors against different types of conveyor faults using a programmable logic controller (plc). This work considers four significant types of faults that commonly occur in conveyors, such as belt sway fault, pull chord fault, zero speed fault, and fire safety.

scholarly journals Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

2021 ◽  
Vol 19 ◽  
pp. 487-492
Author(s):  
Á Encalada-Dávila ◽  
◽  
C. Tutivén ◽  
B. Puruncajas ◽  
Y. Vidal ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Fault Detection ◽  
Wind Turbine ◽  
Sampling Rate ◽  
Prediction Errors ◽  
National Renewable Energy Laboratory ◽  
Different Types ◽  
Detection Strategies ◽  
Types Of Faults

Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

scholarly journals Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

2020 ◽  
Vol 10 (14) ◽  
pp. 4965
Author(s):  
Yordanos Dametw Mamuya ◽  
Yih-Der Lee ◽  
Jing-Wen Shen ◽  
Md Shafiullah ◽  
Cheng-Chien Kuo
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Radial Distribution ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Efficiency Coefficient ◽  
Distribution Grid ◽  
Phase Current ◽  
Three Phase ◽  
Types Of Faults

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

scholarly journals On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3079 ◽  
Author(s):  
Leopoldo Angrisani ◽  
Francesco Bonavolontà ◽  
Annalisa Liccardo ◽  
Rosario Schiano Lo Moriello
Keyword(s):  
Cost Effectiveness ◽  
Long Range ◽  
Distribution Networks ◽  
Fault Isolation ◽  
Optimized Design ◽  
Medium Voltage ◽  
Different Types ◽  
The Times ◽  
The Cost ◽  
Types Of Faults

In this paper, a logic selectivity system based on Long Range (LoRa) technology for the protection of medium-voltage (MV) networks is proposed. The development of relays that communicate with each other using LoRa allows for the combination of the cost-effectiveness and ease of installation of wireless networks with long-range coverage and reliability. The realized demonstrator to assess the proposed system is also presented in the paper; based on different types of faults and different locations, the times needed for clearing a fault and restoring the network were estimated from repeated experiments. The obtained results confirm that, with an optimized design of transmitted packets and of protocol characteristics, LoRa communication grants fault management that meets the criteria of logic selectivity, with fault isolation occurring within the maximum allowed time.

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