Search results for: software fault prediction

Authors: Hyun-Woo Cho

Abstract:

The real-time fault monitoring and diagnosis of large scale production processes is helpful and necessary in order to operate industrial process safely and efficiently producing good final product quality. Unusual and abnormal events of the process may have a serious impact on the process such as malfunctions or breakdowns. This work try to utilize process measurement data obtained in an on-line basis for the safe and some fault-free operation of industrial processes. To this end, this work evaluated the proposed intelligent process data monitoring framework based on a simulation process. The monitoring scheme extracts the fault pattern in the reduced space for the reliable data representation. Moreover, this work shows the results of using linear and nonlinear techniques for the monitoring purpose. It has shown that the nonlinear technique produced more reliable monitoring results and outperforms linear methods. The adoption of the qualitative monitoring model helps to reduce the sensitivity of the fault pattern to noise.

Keywords: process data, data mining, process operation, real-time monitoring

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Authors: F. Amirarfaei, K. Khorasani

Abstract:

In this paper, reconfigurable consensus achievement of a team of agents with marginally stable linear dynamics and single input channel has been considered. The control algorithm is based on a first order linear protocol. After occurrence of a LOE fault in one of the actuators, using the imperfect information of the effectiveness of the actuators from fault detection and identification module, the control gain is redesigned in a way to still reach consensus. The idea is based on the modeling of change in effectiveness as change of Laplacian matrix. Then as special cases of this class of systems, a team of single integrators as well as double integrators are considered and their behavior subject to a LOE fault is considered. The well-known relative measurements consensus protocol is applied to a leaderless team of single integrator as well as double integrator systems, and Gersgorin disk theorem is employed to determine whether fault occurrence has an effect on system stability and team consensus achievement or not. The analyses show that loss of effectiveness fault in actuator(s) of integrator systems affects neither system stability nor consensus achievement.

Keywords: multi-agent system, actuator fault, stability analysis, consensus achievement

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Authors: Toufik Bensana, Medkour Mihoub, Slimane Mekhilef

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, rolling element bearing, local mean decomposition, condition monitoring

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Authors: Ravi Kumar, Dhrubajit Barman, Nomi Baruah

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Software Cloning has grown an active area in software engineering research community yielding numerous techniques, various tools and other methods for clone detection and removal. The copying, modifying a block of code is identified as cloning as it is the most basic means of software reuse. Agile Software Development is an approach which is currently being used in various software projects, so that it helps to respond the unpredictability of building software through incremental, iterative, work cadences. Software Cloning has been introduced to Agile Environment and many Agile Software Development approaches are using the concept of Software Cloning. This paper discusses the various Agile Software Development approaches. It also discusses the degree to which the Software Cloning concept is being introduced in the Agile Software Development approaches.

Keywords: agile environment, refactoring, reuse, software cloning

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Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Ahmad Shukri Mohd Noor, Emma Ahmad Sirajudin, Rabiei Mamat

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Pertaining to a particular Marine interest, the process of data sampling could take years before a study can be concluded. Therefore, the need for a robust backup system for the data is invariably implicit. In recent advancement of Marine applications, more functionalities and tools are integrated to assist the work of the researchers. It is anticipated that this modality will continue as research scope widens and intensifies and at the same to follow suit with current technologies and lifestyles. The convenience to collect and share information these days also applies to the work in Marine research. Therefore, Marine system designers should be aware that high availability is a necessary attribute in Marine repository applications as well as a robust backup system for the data. In this paper, the approach to high availability is related both to hardware and software but the focus is more on software. We consider a NABTIC repository system that is primitively built on a single server and does not have replicated components. First, the system is decomposed into separate modules. The modules are placed on multiple servers to create a distributed system. Redundancy is added by placing the copies of the modules on different servers using Neighbor Caring Environment System(NCES) technique. NCER is utilizing parallel replication components mechanism. A background monitoring is established to check servers’ heartbeats to confirm their aliveness. At the same time, a critical adaptive threshold is maintained to make sure a failure is timely detected using Adaptive Fault Detection (AFD). A confirmed failure will set the recovery mode where a selection process will be done before a fail-over server is instructed. In effect, the Marine repository service is continued as the fail-over masks a recent failure. The performance of the new prototype is tested and is confirmed to be more highly available. Furthermore, the downtime is not noticeable as service is immediately restored automatically. The Marine repository system is said to have achieved fault tolerance.

Keywords: availability, fault detection, replication, fault tolerance, marine application

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Authors: Ikawati Wulandari

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Acquisition of geomagnetic field has been done at Geothermal manifestation Candi Umbul, Grabag, Magelang, Central Java Province on 10-12 May 2013. The purpose of this research to study sub-surface structure condition and the structure which control the hot springs manifestation. The research area have size of 1,5 km x 2 km and measurement spacing of 150 m. Total magnetic field data, the position, and the north pole direction have acquired by Proton Precession Magnetometer (PPM), Global Positioning System (GPS), and of geology compass, respectively. The raw data has been processed and performed using IGRF (International Geomagnetics Reference Field) correction to obtain total field magnetic anomaly. Upward continuation was performed at 100 meters height using software Magpick. Analysis conclude horizontal position of the body causing anomaly which is located at hot springs manifestation, and it stretch along Northeast - Southwest, which later interpreted as normal fault. This hotsprings manifestation was controlled by the downward fault which becomes a weak zone where hot water from underground the geothermal reservoir leakage

Keywords: PPM, Geothermal, Fault, Grabag

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Authors: O. P. Rahi, Manoj Kumar

Abstract:

Power transformers are essential and expensive equipments in electrical power system. Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by conventional method is not always an easy task due to variability of gas data and operational variables. In this paper, an extension theory based power transformer fault diagnosis method is presented. Extension theory tries to solve contradictions and incompatibility problems. This paper first briefly introduces the basic concept of matter element theory, establishes the matter element models for three-ratio method, and then briefly discusses extension set theory. Detailed analysis is carried out on the extended relation function (ERF) adopted in this paper for transformer fault diagnosis. The detailed diagnosing steps are offered. Simulation proves that the proposed method can overcome the drawbacks of the conventional three-ratio method, such as no matching and failure to diagnose multi-fault. It enhances diagnosing accuracy.

Keywords: DGA, extension theory, ERF, fault diagnosis power transformers, fault diagnosis, fuzzy logic

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Authors: T. Bensana, S. Mekhilef

Abstract:

The methodology of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. The ability of wavelet analysis to efficiently detect non-stationary, non-periodic, transient features of the vibration signal makes it a demanding tool for condition monitoring. This paper presents a methodology for fault diagnosis of rolling element bearings based on wavelet envelope power spectrum technique is analysed in both the time and frequency domains. In the time domain the auto-correlation of the wavelet de-noised signal is applied to evaluate the period of the fault pulses. However, in the frequency domain the wavelet envelope power spectrum has been used to identify the fault frequencies with the single sided complex Laplace wavelet as the mother wavelet function. Results show the superiority of the proposed method and its effectiveness in extracting fault features from the raw vibration signal.

Keywords: preventive maintenance, fault diagnostics, rolling element bearings, wavelet de-noising

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Abderrahmen Bouguerra, Kamel Kara, Djamel Saigaa, Samir Zeghlache, Keltoum Loukal

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In this paper, a Fault-Tolerant control of 2 DOF Helicopter (TRMS System) Based on Fuzzy Gain-Adaptive PID is presented. In particular, the introduction part of the paper presents a Fault-Tolerant Control (FTC), the first part of this paper presents a description of the mathematical model of TRMS, an adaptive PID controller is proposed for fault-tolerant control of a TRMS helicopter system in the presence of actuator faults, A fuzzy inference scheme is used to tune in real-time the controller gains, The proposed adaptive PID controller is compared with the conventional PID. The obtained results show the effectiveness of the proposed method.

Keywords: fuzzy control, gain-adaptive PID, helicopter model, PID control, TRMS system

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Authors: Mehmet Hakan Karaata, Ali Hamdan, Omer Yusuf Adam Mohamed

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Consider a distributed system that delivers messages from a process to another. Such a system is often required to deliver each message to its destination regardless of whether or not the system components experience arbitrary forms of faults. In addition, each message received by the destination must be a message sent by a system process. In this paper, we first identify the necessary and sufficient conditions to detect some restricted form of Byzantine faults referred to as modifying Byzantine faults. An observable form of a Byzantine fault whose effect is limited to the modification of a message metadata or content, timing and omission faults, and message replay is referred to as a modifying Byzantine fault. We then present a distributed protocol to detect modifying Byzantine faults using optimal number of messages over node-disjoint paths.

Keywords: Byzantine faults, distributed systems, fault detection, network protocols, node-disjoint paths

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Authors: Kefaya Qaddoum

Abstract:

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Nouredine Benouzza, Ahmed Hamida Boudinar, Azeddine Bendiabdellah

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An algorithm for Eccentricity Fault Detection (EFD) applied to a squirrel cage induction machine is proposed in this paper. This algorithm employs the behavior of the stator current spectral analysis and the localization of the Rotor Slot Harmonic (RSH) frequency to detect eccentricity faults in three phase induction machine. The RHS frequency once obtained is used as a key parameter into a simple developed expression to directly compute the eccentricity fault frequencies in the induction machine. Experimental tests performed for both a healthy motor and a faulty motor with different eccentricity fault severities illustrate the effectiveness and merits of the proposed EFD algorithm.

Keywords: squirrel cage motor, diagnosis, eccentricity faults, current spectral analysis, rotor slot harmonic

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Authors: Mubarak Suliman, Mohamed Hassan

Abstract:

Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection).

Keywords: sensitive earth fault, zero sequence current, grounded system, resistive fault detection, healthy feeder

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Amel Ourici

Abstract:

An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach, using a neural network.

Keywords: doubly fed induction machine, PWM inverter, inter turn stator fault, Park’s vector approach, neural network

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Authors: Aude Gounelle, Gloria Senfaute, Ludivine Saint-Mard, Thomas Chartier

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The long-term deformation rates of faults are not fully captured by Probabilistic Seismic Hazard Assessment (PSHA). PSHA that use catalogues to develop area or smoothed-seismicity sources is limited by the data available to constraint future earthquakes activity rates. The integration of faults in PSHA can at least partially address the long-term deformation. However, careful treatment of fault sources is required, particularly, in low strain rate regions, where estimated seismic hazard levels are highly sensitive to assumptions concerning fault geometry, segmentation and slip rate. When integrating faults in PSHA various constraints on earthquake rates from geologic and seismologic data have to be satisfied. For low strain rate regions where such data is scarce it would be especially challenging. Faults in PSHA requires conversion of the geologic and seismologic data into fault geometries, slip rates and then into earthquake activity rates. Several approaches exist for translating slip rates into earthquake activity rates. In the most frequently used approach, the background earthquakes are handled using a truncated approach, in which earthquakes with a magnitude lower or equal to a threshold magnitude (Mw) occur in the background zone, with a rate defined by the rate in the earthquake catalogue. Although magnitudes higher than the threshold are located on the fault with a rate defined using the average slip rate of the fault. As high-lighted by several research, seismic events with magnitudes stronger than the selected magnitude threshold may potentially occur in the background and not only at the fault, especially in regions of slow tectonic deformation. It also has been known that several sections of a fault or several faults could rupture during a single fault-to-fault rupture. It is then essential to apply a consistent modelling procedure to allow for a large set of possible fault-to-fault ruptures to occur aleatory in the hazard model while reflecting the individual slip rate of each section of the fault. In 2019, a tool named SHERIFS (Seismic Hazard and Earthquake Rates in Fault Systems) was published. The tool is using a methodology to calculate the earthquake rates in a fault system where the slip-rate budget of each fault is conversed into rupture rates for all possible single faults and faultto-fault ruptures. The objective of this paper is to compare the SHERIFS method with one other frequently used model to analyse the impact on the seismic hazard and through sensibility studies better understand the influence of key parameters and assumptions. For this application, a simplified but realistic case study was selected, which is in an area of moderate to hight seismicity (South Est of France) and where the fault is supposed to have a low strain.

Keywords: deformation rates, faults, probabilistic seismic hazard, PSHA

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Authors: Mehmet Hakan Karaata, Ali Hamdan, Omer Yusuf Adam Mohamed

Abstract:

Consider a distributed system that delivers messages from a process to another. Such a system is often required to deliver each message to its destination regardless of whether or not the system components experience arbitrary forms of faults. In addition, each message received by the destination must be a message sent by a system process. In this paper, we first identify the necessary and sufficient conditions to detect some restricted form of Byzantine faults referred to as modifying Byzantine faults. An observable form of a Byzantine fault whose effect is limited to the modification of a message metadata or content, timing and omission faults, and message replay is referred to as a modifying Byzantine fault. We then present a distributed protocol to detect modifying Byzantine faults using optimal number of messages over node-disjoint paths.

Keywords: Byzantine faults, distributed systems, fault detection, network pro- tocols, node-disjoint paths

Procedia PDF Downloads 566

Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

Abstract:

Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

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We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: attractor , cardiac, entropy, holter, mathematical , prediction

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Authors: Jay Xiong, Li Lin

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This paper introduces a new software engineering paradigm based on complexity science, called NSE (Nonlinear Software Engineering paradigm). The purpose of establishing NSE is to help software development organizations double their productivity, half their cost, and increase the quality of their products in several orders of magnitude simultaneously. NSE complies with the essential principles of complexity science. NSE brings revolutionary changes to almost all aspects in software engineering. NSE has been fully implemented with its support platform Panorama++.

Keywords: complexity science, software development, software engineering, software maintenance

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Authors: Saadat Ali, Rashid Abdulla Ahmed Alshehhi

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Cable faults are always probable and common during or after commissioning, causing significant delays and disrupting power distribution or transmission network, which is intolerable for the utilities&service providers being their reliability and business continuity measures. Therefore, the adoption of rapid localization & rectification methodology is the main concern for them. This paper explores the present techniques available for high voltage cable localization & rectification and which is preferable with regards to easier, faster, and also less harmful to cables. It also provides insight experience of high resistance fault locating by utilization of the Very Low Frequency (VLF) method.

Keywords: faults, VLF, real cases, cables

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Authors: Shengxin Yu

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Earthquakes can cause varying degrees of damage to building and bridge structures. Traditional laminated natural rubber bearings (NRB) exhibit inadequate energy dissipation and restraint, particularly under near-fault ground motions, resulting in excessive displacements in the superstructure. This paper presents a composite natural rubber bearing (NFUD-NRB) incorporating two types of shape memory alloy (SMA) U-shaped dampers (UD). The bearing exhibits adjustable features, predominantly characterized by partial self-centering and multi-level energy dissipation, facilitated by nickel-titanium-based SMA (NiTi-SMA) and iron-based SMA (Fe-SMA) UDs. The hysteresis characteristics of NFUD-NRB can be tailored by manipulating the configuration of NiTi-SMA and Fe-SMA UDs. Firstly, the proposed bearing's geometric configuration and working principle are introduced. The rationality of the modeling strategy for the bearing is validated through existing experimental results. Parameterized numerical simulations are subsequently performed to investigate the partially self-centering behavior of NFUD-NRB. The findings indicate that NFUD-NRB can attain the anticipated nonlinear behavior and deliver adequate energy dissipation. Finally, the impact of NFUD-NRB on improving the seismic resilience of highway bridges is examined using the OpenSees software, with particular emphasis on the seismic performance of NFUD-NRB under near-fault ground motions. System-level analysis reveals that bridge systems equipped with NFUD-NRBs exhibit satisfactory residual deformations and higher energy dissipation than those equipped with traditional NRBs. Moreover, NFUD-NRB markedly mitigates the detrimental impacts of near-fault ground motions on the main structure of bridges.

Keywords: partially self-centering behavior, energy dissipation, natural rubber bearing, shape memory alloy, U-shaped damper, numerical investigation, near-fault ground motion

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Authors: Muhammad Irfan Marwat, Sadaqat Jan, Syed Zafar Ali Shah

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Software architecture plays a key role in software development but absence of formal description of software architecture causes different impede in software development. To cope with these difficulties, ontology has been used as artifact. This paper proposes ontology for software architectural design based on IEEE model for architecture description and Kruchten 4+1 model for viewpoints classification. For categorization of style and views, ISO/IEC 42010 has been used. Corpus method has been used to evaluate ontology. The main aim of the proposed ontology is to classify and locate software architectural design information.

Keywords: semantic-based software architecture, software architecture, ontology, software engineering

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Authors: Somayeh Zeinali

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A component is generally defined as a piece of executable software with a published interface. Component-based software engineering (CBSE) has become recognized as a new sub-discipline of software engineering. In the component-based software development, components cannot be completely secure and thus easily become vulnerable. Some researchers have investigated this issue and proposed approaches to detect component intrusions or protect distributed components. Software security also refers to the process of creating software that is considered secure.The terms “dependability”, “trustworthiness”, and “survivability” are used interchangeably to describe the properties of software security.

Keywords: component-based software development, component-based software engineering , software security attributes, dependability, component

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Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

Procedia PDF Downloads 309