A Software Reliability Model for OSS Including Various Fault Data Based on Proportional Hazard-Rate Model

The software reliability model is the stochastic model to measure the software reliability quantitatively. A Hazard-Rate Model is the well-known one as the typical software reliability model. We propose Hazard-Rate Models Considering Fault Severity Levels (CFSL) for Open Source Software (OSS). The purpose of this research is to make the Hazard-Rate Model considering CFSL adapt to baseline hazard function and 2 kinds of faults data in Bug Tracking System (BTS), i.e., we use the covariate vectors in Cox proportional Hazard-Rate Model. Also, we show the numerical examples by evaluating the performance of our proposed model. As the result, we compare the performance of our model with the Hazard-Rate Model CFSL.

OSS Project Assessment Based on Discriminant Analysis and Jump Diffusion Process Model for Fault Big Data

The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can apply to the system testing phase of software development. On the other hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.

Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not.So a discretization algorithm based on particle swarm optimization(PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization(CAIM)discretization method and entropy-based discretization method.

Mechanical Fault Diagnosis Based on Band-phase-randomized Surrogate Data and Multifractal

The vibration signals of machinery with various faults often show clear nonlinear characteristics.Currently,fractal dimension analysis as the common useful method for nonlinear signal analysis,is a kind of single fractal form,which only reflects the overall irregularity of signals,but cannot describe its local scaling properties.For comprehensive revealing of internal properties,a combinatorial method based on band-phase-randomized（BPR） surrogate data and multifractal is introduced.BPR surrogate data method is effective to eliminate nonlinearity in specified frequency band for a fault signal,which can be utilized to detect nonlinear degree in whole fault signal by nonlinear titration method,and the overall nonlinear distribution of fault signal is displayed in nonlinear characteristic curve that can be used to analyze the fault signal qualitatively.Then multifractal theory as a quantitative analysis method is used to describe geometrical characteristics and local scaling properties,and asymmetry coefficient of multifractal spectrum and multifractal entropy for fault signals are extracted as new criterions to diagnose machinery faults.Several typical faults include rotor misalignment,transversal crack,and static-dynamic rubbing fault are analyzed,and the results indicate that those faults can be distinguished by the proposed method effectively,which provides a qualitative and quantitative analysis way in the field of machinery fault diagnosis.

Data-Driven Based Fault Prognosis for Industrial Systems:A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Actuator Fault Detection for Sampled-Data Systems in H_∞ Setting

Actuator fault detection for sampled-data systems was investigated from the viewpoint of jump systems. With the aid of a prior frequency information on fault, such a problem is converted to an augmented H_∞ filtering problem. A simple state-space approach is then proposed to deal with sampled-data actuator fault detection problem. Compared with the existed approaches, the proposed approach allows parameters of the sampled-data system being time-varying with consideration of measurement noise.

Co-seismic fault geometry and slip distribution of the 26 December 2004, giant Sumatra–Andaman earthquake constrained by GPS, coral reef, and remote sensing data

We analyze co-seismic displacement field of the 26 December 2004, giant Sumatra–Andaman earthquake derived from Global Position System observations,geological vertical measurement of coral head, and pivot line observed through remote sensing. Using the co-seismic displacement field and AK135 spherical layered Earth model, we invert co-seismic slip distribution along the seismic fault. We also search the best fault geometry model to fit the observed data. Assuming that the dip angle linearly increases in downward direction, the postfit residual variation of the inversed geometry model with dip angles linearly changing along fault strike are plotted. The geometry model with local minimum misfits is the one with dip angle linearly increasing along strike from 4.3oin top southernmost patch to 4.5oin top northernmost path and dip angle linearly increased. By using the fault shape and geodetic co-seismic data, we estimate the slip distribution on the curved fault. Our result shows that the earthquake ruptured ＊200-km width down to a depth of about 60 km.0.5–12.5 m of thrust slip is resolved with the largest slip centered around the central section of the rupture zone78N–108N in latitude. The estimated seismic moment is8.2 9 1022 N m, which is larger than estimation from the centroid moment magnitude（4.0 9 1022 N m）, and smaller than estimation from normal-mode oscillation data modeling（1.0 9 1023 N m）.

Estimation of the 2001 Kunlun earthquake fault slip from GPS coseismic data using Hori’s inverse method

The Hori＇s inverse method based on spectral decomposition was applied to estimate coseismic slip distribution on the rupture plane of the 14 November 2001 Ms8.1 Kunlun earthquake based on GPS survey results. The inversion result shows that the six sliding models can be constrained by the coseismic GPS data. The established slips mainly concentrated along the eastern segment of the fault rupture, and the maximum magnitude is about 7 m. Slip on the eastern segment of the fault rupture represents as purely left-lateral strike-slip. Slip on the western segment of the seismic rupture represents as mainly dip-stip with the maximum dip-slip about 1 m. Total predicted scalar seismic moment is 5.196× 10^2° N.m. Our results constrained by geodetic data are consistent with seismological results.

Fault detection observer design for networked control system with long time-delays and data packet dropout

Focusing on the networked control system with long time-delays and data packet dropout,the problem of observerbased fault detection of the system is studied.According to conditions of data arrival of the controller,the state observers of the system are designed to detect faults when they occur in the system.When the system is normal,the observers system is modeled as an uncertain switched system.Based on the model,stability condition of the whole system is given.When conditions are satisfied,the system is asymptotically stable.When a fault occurs,the observers residual can change rapidly to detect the fault.A numerical example shows the effectiveness of the proposed method.

Interpretation of the west segment of the coastal fault zone in the coastal region of South China based on the gravity data

By systemic processing, comprehensive analysis, and interpretation of gravity data, we confirmed the existence of the west segment of the coastal fault zone(west of Yangjiang to Beibu Bay) in the coastal region of South China. This showed an apparent high gravity gradient in the NEE direction, and worse linearity and less compactness than that in the Pearl River month. This also revealed a relatively large curvature and a complicated gravity structure. In the finding images processed by the gravity data system, each fault was well reflected and primarily characterized by isolines or thick black stripes with a cutting depth greater than 30 km. Though mutually cut by NW-trending and NE-trending faults, the apparent NEE stripe-shaped structure of the west segment of the coastal fault zone remained unchanged,with good continuity and an activity strength higher than that of NW and NE-trending faults. Moreover,we determined that the west segment of the coastal fault zone is the major seismogenic structure responsible for strong earthquakes in the coastal region in the border area of Guangdong, Guangxi, and Hainan.

Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration

The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.

Data Processing of Fault Recorder in Power System

This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.

Deformation Evolution Characteristics Revealed by GPS and Cross-fault Leveling Data before the M_S8. 0 Wenchuan Earthquake

Based on GPS velocity during 1999-2007,GPS baseline time series on large scale during1999-2008 and cross-fault leveling data during 1985-2008,the paper makes some analysis and discussion to study and summarize the movement,tectonic deformation and strain accumulation evolution characteristics of the Longmenshan fault and the surrounding area before the MS8. 0 Wenchuan earthquake,as well as the possible physical mechanism late in the seismic cycle of the Wenchuan earthquake. Multiple results indicate that:GPS velocity profiles show that obvious continuous deformation across the eastern Qinghai-Tibetan Plateau before the earthquake was distributed across a zone at least 500 km wide,while there was little deformation in Sichuan Basin and Longmenshan fault zone,which means that the eastern Qinghai-Tibetan Plateau provides energy accumulation for locked Longmenshan fault zone continuously. GPS strain rates show that the east-west compression deformation was larger in the northwest of the mid-northern segment of the Longmenshan fault zone,and deformation amplitude decreased gradually from far field to near fault zone,and there was little deformation in fault zone. The east-west compression deformation was significant surrounding the southwestern segment of the Longmenshan fault zone,and strain accumulation rate was larger than that of mid-northern segment.Fault locking indicates nearly whole Longmenshan fault was locked before the earthquake except the source of the earthquake which was weakly locked,and a 20 km width patch in southwestern segment between 12 km to 22. 5 km depth was in creeping state. GPS baseline time series in northeast direction on large scale became compressive generally from 2005 in the North-South Seismic Belt,which reflects that relative compression deformation enhances. The cross-fault leveling data show that annual vertical change rate and deformation trend accumulation rate in the Longmenshan fault zone were little,which indicates that vertical activity near the fault was very weak and the fault was tightly locked. According to analyses of GPS and cross-fault leveling data before the Wenchuan earthquake,we consider that the Longmenshan fault is tightly locked from the surface to the deep,and the horizontal and vertical deformation are weak surrounding the fault in relatively small-scale crustal deformation. The process of weak deformation may be slow,and weak deformation area may be larger when large earthquake is coming. Continuous and slow compression deformation across eastern Qinghai-Tibetan Plateau before the earthquake provides dynamic support for strain accumulation in the Longmenshan fault zone in relative large-scale crustal deformation.

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

Automatic Fault Prediction of Wind Turbine Main Bearing Based on SCADA Data and Artificial Neural Network

As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.

DATA-MINING BASED FAULT DETECTION

This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test, by which the quasi-optimal embedding dimension and time delay can be obtained.The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.

Analysis of an Active Fault Geometry Using Satellite Sensor and DEM Data: Gaziköy-Saros Segment (NAFZ), Turkey

In this study, Landsat 5 Thematic Mapper (TM) and SPOT HRV Panchromatic data were analysed to determine the geometry of an active fault segment (the Ganos segment) in Gazikoy-Saros region, west of Marmara Sea, Turkey. Gazikoy-Saros/Ganos segment is a part of North Anatolian Fault Zone (NAFZ). North-Anatolian fault is considered to be one of the most important active strike-slip faults in the world. Thus far in relevant researches based on Gazikoy-Saros segment a single straight fault line representation is used on the fault descriptive geological maps. This study, with the aid of enhanced remotely sensed data aims to reveal the linear details of the NAFZ fault segment, which subsequently were superposed with a Digital Elevation Model (DEM) data. Respectively, using these data the surface geometry expression of Gazikoy-Saros fault segment was detailed and remapped. According to the results of the analysis two small releasing steps were identified on this segment. The first one is situated between Mürseli and Güzelkoy villages, and the second one is between Mürseli and Yorguc villages. In addition to this, it is found that the fault strike bends approximately 7° further to in south-eastern (SE) direction between Yenikoy and Sofular villages. This angular change was defined with the advantage of multi-angular viewing capability of the multi-satellite sensors and DEM data. The newly generated surface geometry expression of Ganos segment was compared with Global Positioning System (GPS) velocity vectors.

Offshore Fault Geometrics in the Pearl River Estuary,Southeastern China:Evidence from Seismic Reflection Data

The Pearl River Estuary(PRE) is located at the onshore-offshore transition zone between South China and South China Sea Basin, and it is of great significant value in discussing tectonic relationships between South China block and South China Sea block and seismic activities along the offshore active faults in PRE. However, the researches on geometric characteristics of offshore faults in this area are extremely lacking. To investigate the offshore fault distribution and their geometric features in the PRE in greater detail, we acquired thirteen seismic reflection profiles in 2015. Combining the analysis of the seismic reflection and free-air gravity anomaly data, this paper revealed the location, continuity, and geometry of the littoral fault zone and other offshore faults in PRE. The littoral fault zone is composed of the major Dangan Islands fault and several parallel, high-angle, normal faults, which mainly trend northeast to northeast-to-east and dip to the southeast with large displacements. The fault zone is divided into three different segments by the northwest-trending faults. Moreover, the basement depth around Dangan Islands is very shallow, while it suddenly increases along the islands westward and southward. These has resulted in the islands and neighboring areas becoming the places where the stress accumulates easily. The seismogenic pattern of this area is closely related to the comprehensive effect of intersecting faults together with the low velocity layer.