Failure Prediction

Competing notions of biometric recognition system failure prediction have emerged recently, which can roughly be categorized as quality and non-quality based approaches. Quality, while well correlated overall with recognition performance, is a weaker indication of how the system will perform in a particular instance -something of primary importance for critical installations, screening areas, and surveillance posts. An alternative approach, incorporating a Failure Prediction Receiver Operator Characteristic (FPROC) analysis has been proposed to overcome the limitations of the quality approach, yielding accurate predictions on a per instance basis.

A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists for the development of business failure prediction models that assess the financial condition of firms sequentially over time. This article addresses this need by presenting a sequential business failure prediction model.

This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN approaches, can outperform traditional statistical models for predicting corporate failures in Australia one year and two years prior to the financial distress. The results suggest that hybrid neural networks outperform all other models one and two years before failure. Therefore, hybrid neural network model is a very promising tool for failure prediction. This supports the conclusion that for shareholders, policymakers and others interested in early warning systems, hybrid networks would be useful.

This work investigates the behavior under burst pressure testing of a pressure vessel liner. The liner was produced with a polymer blend of 95 wt.% low linear density polyethylene (LLDPE) and 5 wt.% of high density polyethylene (HDPE). The liner is to be used in an allcomposite carbon/epoxy compressed natural gas (CNG) shell, manufactured by the filament winding process, with variable composite thickness. Experimental hydrostatic tests were conducted on reduced scale and actual liner models. Design and failure prediction of the composite laminate shell and the polymeric liner were conducted based on Tsai-Wu and von Mises criteria, respectively, using commercial Finite Element Analysis (FEA) software. Simulation and testing were both important in order to define adequate production parameters for the polymeric liner so that it could be successfully used in a composite pressure vessel.

Business failure prediction is one of the most essential problems in the field of finance. The research on developing business failure prediction models has been focused on building classification models to distinguish among failed and non-failed finns. Such models are of major importance to financial decision makers (credit managers, managers of firms, investors, etc.); they serve as early warning systems of the failure probability of a corporate entity. The significance of business failure prediction models has been a major motivation for researchers to develop efficient approaches for the development of such models. This paper considers several such approaches, including multicriteria decision aid (MCDA) techniques, linear programming and performs a thorough comparison to traditional statistical techniques such as linear discriminant analysis and logit analysis. The comparison is performed using a sample of 144 US firms for a period of up to five years prior to failure.

All damage and failure models, describing either the evolution of microvoids, the development of shear bands or local rupture, rely on the knowledge of the hardening function at large plastic strains which, then, becomes an essential prerequisite for any failure prediction.

This paper presents a constitutive wood model that accounts for both hardening associated with material densification at large compressive deformations and brittle failure modes. The model is adapted from previous work by the authors and has been modified to deal with wood behaviours. The main novelty of the model is the coupling between the anisotropic plasticity and the ductile densification. The model developed is successfully implemented in the commercial ABAQUS software. Validation was made for uniaxial compressive loadings and an application on a three-points bending test. The results obtained, for the uniaxial compressive loadings, demonstrate the capability of the model to simulate the wood behaviour at large compressive deformations and show clearly the effect of the densification on the plastic behaviour. The result obtained for the three-points bending test shows a good implementation of the brittle failure criterion and demonstrates the suitability of the developed model to analyse and design wooden structures.

This study explores applications of the failure assessment diagram (FAD) methodology to predict the failure behaviour for high pressure pipelines with planar defects having different geometries (i.e., crack depth and crack length). One purpose of this investigation is to assess the capability of FAD procedures in integrity analyses of high pressure pipelines with varying crack configurations. Another purpose is to address the effectiveness of constraint-based FADs to predict burst pressure of low-constraint cracked pipelines. Full scale burst testing of end-capped pipe specimens with axial surface flaws provide the data needed to compare the failure predictions derived from the FAD procedures. The analyses reveal that the degree of agreement between predicted pressures and experimentally measured values depends rather markedly on the crack size for the tested pipes. Moreover, the analyses also show a possible weak dependence of the predicted pressures on the constraint-based correction scheme. Overall, the results validate the use of FAD-based methodologies for defect assessments of axially cracked pipelines. r

This article introduces definitions of the terms bankruptcy, corporate failure, insolvency, as well as the methods of bankruptcy, and popular economic failure prediction models. We will show that a firm filing for corporate insolvency does not necessarily fail to pay off its financial obligations as they mature. Moreover, we will assume an appropriate risk monitoring system centered by well-developed failure prediction models, which is crucial to various parties in the investment world as a means to look after the financial future of their clients or themselves.

Formability of sheet metal is usually assessed by the useful concept of forming limit diagrams (FLD) and forming limit curves (FLC) represent a first safety criterion for deep drawing operations. The level of FLC is strongly strain path dependent as observed by experimental and numerical results and therefore nonproportional strain paths need to be incorporated when analyzing formability of sheet metal components. Simulations using finite element method allow accurate predictions of stress and strain distributions in complex stamped parts. However, the prediction of localized necking is a difficult task and the combination of forming limit diagram analysis with finite element simulations often fail to give the right answer, if complex strain paths are not included in these predictions.

A fracture mechanics-based model for fatigue failure prediction of adhesive joints has been applied in this work. The model is based on the integration of the kinetic law of evolution of defects originated at stress concentrations within the joint. Final failure can be either brittle (fracture toughness-driven) or ductile (tensile/shear strength-driven) depending on the adhesive. The model has been validated against experiments conducted on single-lap shear joints bonded with a structural adhesive. Three different kinds of adhesives, namely a modified methacrylate, a one-part epoxy and a two-part epoxy supplied by Henkel, have been considered and three different overlap lengths have been tested. Fracture toughness and fatigue crack growth properties of the adhesives have been determined with mode I tests. The number of cycles to failure has been successfully predicted in several cases. It is interesting to notice that in the case of joints loaded at the same average shear stress, the shorter the joint, the longer the duration. This fact is also captured by the model.

Reliability test is a very important step for any electronic products before they can be sold to the market. In the previous decades, various types of test have been developed. One of the widely used testing methods is the board bending test. A major issue of performing a reliability test is that numbers of testing samples are required since the test has to be repeated until consistent and satisfactory results can be obtained. A long time will be spent on the specimen design, the assembly processes and the testing procedures. In case the reliability test is not satisfied, the specimen will need to be redesigned, reproduced and retested. This is costly and not efficient from the commercial point of view. To simplify the reliability testing process, computational simulation is proposed to predict the reliability of printed circuit board assemblies (PCBA) at the early design stage. In the present study, 3-point bending test is the reliability test adopted. Finite element model validation and two case studies regarding the application of the model are the objectives of this work. Comparison between experimental results and computational results is carried out for model validation. The physical properties (stiffness and strain) of the model show a good agreement with the experiments at both the elastic deformation stage and the plastic deformation stage under the 3-point bending condition. The maximum von Mises stress at the solder joints of the model at which the samples are found to be failed is also very close to the strength of the solder. This further validates the model. For application, this stress data is used as a failure criterion to indicate whether a PCBA is reliable under different bending conditions

Recently, the use and the applicability of reliability prediction models for microelectronic devices using the temperature-dependent Eyring and Arrhenius function, have been criticized. In fact, the temperature dependence of failure mechanisms of microelectronic devices and electronic equipment in general has been questioned. This paper discusses the effect of temperature on microelectronic failure mechanisms at various device failure sites. This paper also presents information on those mechanisms which may be more appropriately modelled by functions dependent on temperature change, the rate of temperature change and spatial temperature gradients.

In tube hydroforming, circular components are hydrobulged or hydroformed from tubular blanks with internal pressure and simultaneous axial loading. Thus the tube can be fed into the deformation zone during the bulge operation allowing more expansion and less thinning without any defects such as wrinkling, buckling, and bursting. By contrast with the buckling and the wrinkling, the bursting is generally classified as an irrecoverable failure mode. Hence in order to obtain the sound hydroformed products, it is necessary to predict the bursting behavior and to analyze the effects of process parameters on this failure condition in hydroforming processes. In this study, a forming limit stress diagram (FLSD) is constructed by plotting the calculated principal stresses based on the local necking criterion. Using the theoretical FLSD, we carry out the numerical prediction of bursting failure in a hydroforming process, which usually has non-linear strain path. Finite element analyses are carried out to find out the state of stresses during simple hydroforming operation, in which the FLSD is utilized as the forming limit criterion for assessment of the initiation of necking, and influences of the material parameters on the formability are investigated. In addition, the numerical results obtained from the FEM combined with the FLSD are confirmed with a series of bulge tests in view of bursting pressure and show a good agreement. Consequently, it is shown that the theoretical and numerical approach to bursting failure prediction proposed in this paper will provide a feasible method to satisfy the increasing practical demands for assessment of the forming severity in hydroforming processes.

The authors examined relations between implicit and explicit Hope of Success (HS) and Fear of Failure (FF) and memory and liking for successful and unsuccessful peers. Implicit motives were expected to predict memory and explicit motives to predict liking of peers. Results from 106 American and 79 Singaporean students supported the implicit-explicit motive distinction as well as a link between HS and the successful peer and between FF and the unsuccessful peer. In both samples, explicit HS predicts liking while implicit HS predicts memory about the successful peer, and implicit FF predicts memory about the unsuccessful peer. Findings indicate achievement motives affect interest in and reaction towards peers and provide evidence for differential validity of implicit and explicit motives.

Asbestos cement (AC) pipes are among the oldest assets in many water supply networks. With increasing failure rates and cost consequences, asset management tools are required to pre-empt unplanned failures and schedule future replacement at the most cost-effective time during service. This paper develops a physical probabilistic failure model for AC pipes under combined internal pressure and external loading. Uncertainty in the degradation process is accounted for using the Weibull extreme value probability distribution. Monte Carlo simulation is then used to estimate the probability of pipe failure as ageing proceeds. In the final stage of the model, a cost/benefit analysis is conducted to determine optimal scheduling of future inspection and replacement activities.

A growing number of structural applications and repairs require strong adhesively bonded joints between composite parts. The viability of the finite elements method (FEM) for the design and analysis of such joints has been shown by several researchers (Composites 13(1982) 29, mechanics and mechanisms of damage in composites and multi-materials, ESIS11, Mechanical Engineering Publications, London, 1991, Structural adhesive joints in engineering, Elsevier Applied science Publishers, Amsterdam, 1984). Most of the proposed plane stress models for adhesively bonded joints have traditionally relied on effective lamina properties for the composite adherends because one of the primary goals was the study of adhesive stresses and failure criteria (J. Adhes. 42 (1993)). Such work has also been usually carried out on mainframe computers. However, with the rapid developments in computing, commercial FE codes now exists for PC applications. These present new possibilities, namely the construction of models which are closer in their approximation to the structures they represent. Within this context, a new 2D plane stress approach has been developed which allows ply-by-ply modelling of composite adherends in a joint. It is a development of the traditional 2D plane stress model used by several authors such as Siener (Compos. Mater.: Testing Des. 10 (1992) 444) in investigating of composite repair joints.This paper reports the application of this new approach to the modelling of scarf joints between composite adherends under tensile loading. Various scarf angles were investigated and ultimate joint failure predicted. The results of the analysis were compared to the experimental results published by Adkins and Pipes (Proceedings of the Fourth Japan–US Conference on Composite Materials, 1988, pp. 845–854). The main aim of work was to develop a model which could provided more information than conventional models.The new approach offers the prospect of accurately modelling more realistic joints because, through the retention of the laminated nature of the composite adherends, scope now exists for the application of more physically based failure criteria.This work has shown that it is possible to model adhesively bonded composite joints using an improved 2D plane stress model which retains the laminated nature of the composite adherends. There was good agreement between the predicted failure loads and the experimental results for 3.0°, 6.2° and 9.2° scarf joints. These predictions are dependent on the type of failure criteria used. For the composite adherends, the maximum stress criterion faired better in predicting the 3.0° scarf joint strength.

The structural integrity assessment of engineering plant is based on the principles of fracture mechanics that assume defects to be sharp cracks. Whilst conservative, this assumption may be overly conservative in some situations, e.g. for defects that are non-sharp. This paper describes the prediction of cleavage fracture initiation from blunt notches of varying root radii using the Weibull stress based toughness scaling model. Failure predictions are compared with the results of experiments performed on single edge notch bend SE(B) specimens containing both cracks and notches of varying notch root radii. Cleavage initiation sites were located close to the peak tensile stress ahead of the notch, implying that a tensile stress criterion is the main controlling factor for cleavage fracture. The cleavage fracture predictions from the toughness scaling model correlate well with the experimental data; but care needs to be taken to ensure that calibration of the Weibull parameters references fracture toughness data from constraint levels that span that of the defect of interest. This ensures the model interpolates between the constraint states used for calibration, rather than extrapolating outside the range of applicability.

Recent developments in the aircraft industry towards substantially improving fuel economy and extending flight range have accelerated interest in the use of advanced composites as primary structural materials. The airframes of next-generation airliners will have substantial parts made of light-weight composites. This means that the engineering demands on the performance of fiber-reinforced composites will become greater. There is therefore a need to better understand and predict the multiple complex failure mechanisms in composite structures, and to devise more reliable failure theories and damage progression models. There is a large body of literature on progressive damage analysis in composites, much of which employs damage mechanics and material stiffness degradation methods. This article reviews some of the more recent work in this area and describes the issues pertinent to application in composite structures. The authors' ongoing research efforts in modeling and prediction ...

Previous experimental evidence has shown scale sensitivity of adhesives both in terms of ultimate stress and strain to failure; this sensitivity may be modelled using Weibull statistics. The implications of this on the sensitivity of strength predictions to mesh density around singularity points and small changes in local geometry is investigated using an idealised model of an enclosed corner in an adhesive joint. The sensitivity of two other failure criteria are also compared to the Weibull method. It is seen that there is a critical relationship between the Weibull shape parameter, m, and the singularity strength, . This relationship marks the transition between mesh insensitivity and mesh sensitivity, even with a singular stress (or strain) field present. If the singularity is removed by localised rounding, then the Weibull method of failure prediction shows much less sensitivity to the exact local geometry variation than other methods of failure prediction.

The goal of this study is to show an alternative method to corporate failure prediction. In the last decades Artificial Neural Networks have been widely used for this task. These models have the advantage of being able to detect non-linear relationships and show a good performance in presence of noisy information, as it usually happens, in corporate failure prediction problems. AdaBoost is a novel ensemble learning algorithm that constructs its base classifiers in sequence using different versions of the training data set. In this paper, we compare the prediction accuracy of both techniques on a set of European firms, considering the usual predicting variables such as financial ratios, as well as qualitative variables, such as firm size, activity and legal structure. We show that our approach decreases the generalization error by about thirty percent with respect to the error produced with a neural network.

Soft storey buildings are characterized by having a storey which has a lot of open spaces. This soft storey creates a major weak point in an earthquake. Since soft stories are classically associated with retail spaces and parking garages, they are often on the lower stories of a building, which means that when they collapse, they can take the whole building down with them, causing serious structural damage which may render the structure totally unusable. In this study, efforts have been given to examine the effect of incorporation of isolator on the seismic behavior of buildings subjected to the appropriate earthquake for medium risk seismicity region. It duly ensures incorporating isolator with all relevant properties as per respective isolators along with its time period and damping ratio. Effort has also been made here to build up a relationship for increasing storey height and the changes for incorporating isolator with same time period and damping ratio for both lead rubber bearing (LRB) and high damping rubber bearing (HDRB). Dynamic analyses have been carried out using response spectrum and time history analysis. Behavioral changes of structural parameters are investigated. The study reveals that the values of structural parameters reduce a large amount while using isolator. However, LRB is found beneficial than HDRB. The structure experiences huge storey drift at the soft storey level that may be severe and cause immature failure. The amount of masonry infill is very vital for soft storey buildings as its decrement increases reasonable displacements.

Gompertz curve has been used to estimate the number of residual faults in testing phases of software development, especially by Japanese software development companies. Since the Gompertz curve is a deterministic function, the curve cannot be applied to estimating software reliability which is the probability that software system does not fail in a prefixed time period. In this article, we propose a stochastic model called the Gompertz software reliability model based on non-homogeneous Poisson processes. The proposed model can be derived from the statistical theory of extreme-value, and has a similar asymptotic property to the deterministic Gompertz curve. Also, we develop an EM algorithm to determine the model parameters effectively. In numerical examples with software failure data observed in real software development projects, we evaluate performance of the Gompertz software reliability model in terms of reliability assessment and failure prediction.

As failure criterion for sheet metal forming, conventional forming limit diagrams (FLD) are often used. The FLD is a strain based criterion, which evaluates the principal deformations at failure. Different investigations show that the FLD is dependent on the forming history and strain path. However, this is not the case for forming limit stress diagrams (FLSD). For this failure criterion, the principal stresses at failure are determined by FEM simulation of the Nakazima-test. Both, the FLD and the FLSD experimental investigations provide the basis for the sheet metal failure criterion. In contrast, continuum damage mechanics describe the damage evolution in the microstructure with physical equations, so that crack initiation due to mechanical loading can be predicted. By using the Gurson–Tvergaard–Needleman (GTN) damage mechanical model, a failure criterion based on void evolution was examined in this work. The parameter identification for the damage model will be discussed. The investigations demonstrate that FLD is inapplicable for complex forming processes with strain path changes. The FLSD is better suitable than the FLD for multi step forming processes. Micro-mechanical damage modelling with the GTN model also shows acceptable formability predictions, in spite of its generally insufficient consideration of the effective deviatoric stress fractions. The quality of failure prediction using continuum damage mechanics models is able to be increased by applying a more suitable damage models and a modern overall-scale modelling approach.

Generally faults in complex technical systems (such as aircrafts) can be considered as rare events. In this paper we apply classification techniques to problem of rare events anticipation and demonstrate the approach to predictive maintenance of aircrafts through the real-world test cases from aircraft operations based on the data granted by AIRBUS.

This work investigates the behavior under burst pressure testing of a pressure vessel liner. The liner was produced with a polymer blend of 95 wt.% low linear density polyethylene (LLDPE) and 5 wt.% of high density polyethylene (HDPE). The liner is to be used in an allcomposite carbon/epoxy compressed natural gas (CNG) shell, manufactured by the filament winding process, with variable composite thickness. Experimental hydrostatic tests were conducted on reduced scale and actual liner models. Design and failure prediction of the composite laminate shell and the polymeric liner were conducted based on Tsai-Wu and von Mises criteria, respectively, using commercial Finite Element Analysis (FEA) software. Simulation and testing were both important in order to define adequate production parameters for the polymeric liner so that it could be successfully used in a composite pressure vessel.

This paper presents a low-cost method to realize a real-time condition monitoring and a predictive-maintenance system of an electrolytic capacitor used in uninterruptible power supplies (UPSs). This method consists in detecting the changes in real time of the equivalent series resistance and the capacitance C values of the electrolytic capacitors. Simulation and experimental results are presented to illustrate the proposed monitoring technique. The proposed method can be used in UPS where waveforms are continuously varying in amplitude, frequency, and temperature. The proposed online failure prediction method has the merits of using only the existent resources in UPS and with the use of known algorithms.

There are significant differences in glass strength information presented by major United States glass manufacturers. These differences have caused considerable confusion among glass designers. The differences are the result of different simplifying assumptions in formulation of the respective glass design procedures. Presented in this paper is a glass failure prediction model which relates the probability of failure of glass to surface flaw characteristics and induced stresses. This is accomplished by combining a statistical failure theory for brittle materials with results of a geometrically nonlinear plate analysis. The model is formulated to include all factors which are known to significantly affect the strength of glass, such as load duration, surface area, etc. As such the model provides a basis for resolving current conflicts in the glass design process.

Exchange rate misalignment (which is a prelude to financial crisis), macroeconomic volatility, linear and non-linear exchange rate exposure, financial crisis, contagion, and spillovers are key vices that often pose significant threats to corporate survival. In view of this, this paper shows how the 1966 corporate failure models failed to reflect the challenges of the 21st century's economic dynamics and experiences of corporate failure, particularly in Asia. The paper exposes the weakness of the 1966 models and makes the case for the need for more cogent models that can parsimoniously capture lingering and untamable systematic risks in an economy. To do this, we argue that there is a chicken and egg relationship between corporate failure and the range of financial and macroeconomic crisis indicators in order to determine if corporate failures can be efficiently predicted by ratios or by exogenously induced catastrophic factors. We do this by applying the teaching and philosophy of the concept of catastrophe theory. The result of our theoretical and conceptual analysis indicates that modern corporate failure prediction is not able to rely on the ordinary ratio. As a result of this, we recommend and establish a procedure for an ex ante econometric estimation and prediction model of corporate failure that has generally encompassing features relevant particularly during an incoming financial crisis.

Large-scale systems like BlueGene/L are susceptible to a number of software and hardware failures that can affect system performance. In this paper evaluate the effectiveness of a previously developed job scheduling algorithm for BlueGene/L in the presence of faults. We have developed two new job-scheduling algorithms considering failures while scheduling the jobs. We have also evaluated the impact of these algorithms on average bounded slowdown, average response time and system utilization, considering different levels of proactive failure prediction and prevention techniques reported in the literature. Our simulation studies show that the use of these new algorithms with even trivial fault prediction confidence or accuracy levels (as low as ½¼±) can significantly improve the performance of the BlueGene/L system.

Electrochemical oxygen sensors are widely used for industrial hygiene applications to monitor potentially dangerous conditions due to oxygen de®ciency or excess. Oxygen sensors of the galvanic type operate by consumption of an internal, easily oxidizable anode. Once the entire anode has been consumed, the sensor will no longer respond to oxygen and this failure of the sensor is often rapid and with little warning. This paper describes an electrical test, consisting of measuring the ratio of the steady state current at an applied potential of $0.4 V vs. the steady state current at 0 V. A ratio close to unity indicates that the sensor is functioning normally, whereas a ratio much less than unity indicates that the sensor will fail soon. This test may be performed in situ on the oxygen sensor by a gas detection instrument, which provides a means for the detection of an imminent failure, while the sensor is still operational.

A study of the strain/stress gradient effect in the formability of stretch-bend metal sheets is presented. Depending on the severity of these gradients, two independent kinds of failure can be defined: Necking-and Fracturecontrolled Failure. In a previous work the authors assumed that the former failure mode takes place when the inner fibre necks, and the latter mode arises when the outer fibre of the sheet fractures. Obviously, this assumption could seem a priori too much restrictive. Both types of failure are remodelled in the present work, but now it assumes that failure is controlled by the evolution of the strains and stresses in a certain material volume at the inner and the outer surface of the sheet. The size of this critical volume and their effects in the failure predictions are discussed.

Full realization of all the potentials of predictive maintenance highly depends on the accuracy of long-term predictions of the remaining useful life of manufacturing equipments. In this paper, we propose a new method that is capable of achieving high long-term prediction accuracy by comparing signatures from any two degradation processes using measures of similarity that form a match matrix (MM). Through this concept, we can effectively include large amounts of historical information into the prediction of the current degradation process. Similarities with historical records are used to generate possible future distributions of features indicative of process performance, which are then used to predict the probabilities of failure over time by evaluating overlaps between predicted feature distributions and feature distributions related to unacceptable equipment behavior. The analysis of experimental results shows that the proposed method can yield a noticeable improvement of long-term prediction accuracy in terms of mean prediction errors over the Elman Recurrent Neural Network (ERNN) based prediction, which was shown in the past literature to predict well behavior of highly non-linear and non-stationary time series. #

We present a case study of a difficult real-world pattern recognition problem: predicting hard drive failure using attributes monitored internally by individual drives. We compare the performance of support vector machines (SVMs), unsupervised clustering, and non-parametric sta- tistical tests (rank-sum and reverse arrangements). Some- what surprisingly, the rank-sum method outperformed the other methods, including SVMs. We also show the utility

A numerical material model for intra-laminar failure prediction of biaxial Non Crimp Fabric composites is presented. The model combines the elasto-plastic continuum damage constitutive model proposed by Ladevèze with the intra-laminar matrix failure model of Puck, and improves the shear damage representation using an exponential damage function. The work focuses on numerical model development for damage and failure prediction and validation for both unsheared and sheared fabric composites which will result from draping of the fabric preform over a double curved geometry in order to manufacture complex shaped parts. An extensive test program has been conducted using flat composite coupons with differing degrees of fabric pre-shear in order to establish a database of material stiffness, strength, and in-plane matrix shear damage evolution prior to failure. This database is used to identify parameters for the proposed 'Ladevèze-Puck' damage and failure model. Final validation has been made by implementing the model in

As the scale of parallel systems continues to grow, fault management of these systems is becoming a critical challenge. While existing research mainly focuses on developing or improving fault tolerance techniques, a number of key issues remain open. In this paper, we propose runtime strategies for spare node allocation and job rescheduling in response to failure prediction. These strategies, together with failure prediction and fault tolerance techniques, construct a runtime system called Fault-Aware Runtime System (FARS). In particular, we propose a 0-1 knapsack model and demonstrate its flexibility and effectiveness for reallocating running jobs to avoid failures. Experiments, by means of synthetic data and real traces from production systems, show that FARS has the potential to significantly improve system productivity (i.e., performance and reliability).

Some volcanoes exhibit at times accelerating strain and seismicity before eruptions. Different authors have used this behavior to predict eruptions and to develop materials-science models. Here, a linear Kelvin-Voigt viscoelastic model is proposed to describe the observed behavior. This model also has applications to other failure phenomena, such as landslides. The strain rate of a variety of materials under constant stress after an instantaneous elastic response is found to be well described by a general relaxation law: