Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network has become exceedingly serious. As urban water supply network is hidden underground asset, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network, decision tree algorithm (C4.5) has been carried out to classify the specific situation of water supply pipeline. Part of the historical data was used to establish a decision tree classification model, and the remaining historical data was used to validate this established model. Adopting statistical methods were used to access the decision tree model including basic statistical method, Receiver Operating Characteristic (ROC) and Recall-Precision Curves (RPC). These methods has been successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model was to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable (AU), near perfect condition (NPC) and serious deterioration (SD). Finally, this research focused on pipe classification which plays a significant role in maintaining water supply networks in the future.

Biomechanical evaluation of internal and external hexagon platform switched implant-abutment connections: An in vitro laboratory and three-dimensional finite element analysis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Effect of implant connection and restoration design (screwed vs. cemented) in reliability and failure modes of anterior crowns

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analytical and Monte Carlo approaches to evaluate probability distributions of interruption duration

Regulatory authorities in many countries, in order to maintain an acceptable balance between appropriate customer service qualities and costs, are introducing a performance-based regulation. These regulations impose penalties-and, in some cases, rewards-that introduce a component of financial risk to an electric power utility due to the uncertainty associated with preserving a specific level of system reliability. In Brazil, for instance, one of the reliability indices receiving special attention by the utilities is the maximum continuous interruption duration (MCID) per customer.This parameter is responsible for the majority of penalties in many electric distribution utilities. This paper describes analytical and Monte Carlo simulation approaches to evaluate probability distributions of interruption duration indices. More emphasis will be given to the development of an analytical method to assess the probability distribution associated with the parameter MCID and the correspond ng penalties. Case studies on a simple distribution network and on a real Brazilian distribution system are presented and discussed.

Reliability and failure modes of internal conical dental implant connections

Objective: Biological and mechanical implant-abutment connection complications and failures are still present in clinical practice, frequently compromising oral function. The purpose of this study was to evaluate the reliability and failure modes of anterior single-unit restorations in internal conical interface (ICI) implants using step-stress accelerated life testing (SSALT). Materials and methods: Forty-two ICI implants were distributed in two groups (n = 21 each): group AT-OsseoSpeed™ TX (Astra Tech, Waltham, MA, USA); group SV-Duocon System Line, Morse Taper (Signo Vinces Ltda., Campo Largo, PR, Brazil). The corresponding abutments were screwed to the implants and standardized maxillary central incisor metal crowns were cemented and subjected to SSALT in water. Use-level probability Weibull curves and reliability for a mission of 50,000 cycles at 200 N were calculated. Differences between groups were assessed by Kruskal-Wallis along with Bonferroni's post-hoc tests. Polarized-light and scanning electron microscopes were used for failure analyses. Results: The Beta (β) value derived from use level probability Weibull calculation was 1.62 (1.01-2.58) for group AT and 2.56 (1.76-3.74) for group SV, indicating that fatigue was an accelerating factor for failure of both groups. The reliability for group AT was 0.95 and for group SV was 0.88. Kruskal-Wallis along with Bonferroni's post-hoc tests showed no significant difference between the groups tested (P > 0.27). In all specimens of both groups, the chief failure mode was abutment fracture at the conical joint region and screw fracture at neck's region. Conclusions: Reliability was not different between investigated ICI connections supporting maxillary incisor crowns. Failure modes were similar. © 2012 John Wiley & Sons A/S.

Mechanical Evaluation of Four Narrow-Diameter Implant Systems

Purpose: This study aimed to evaluate the survival probability of four narrow-diameter implant systems when subjected to fatigue loading. Materials and Methods: Seventy-two narrow-diameter implants to be restored with single-unit crowns were divided into four groups (n = 18): Astra Tech (3.5-mm diameter), with a standard connection (ASC); BioHorizon (3.4-mm diameter), with a standard connection (BSC); Intra-Lock (3.4-mm diameter), with a standard multilobular connection (ISC); and Intra-Lock (3.4-diameter), with a modified square connection (IMC). The corresponding abutments were screwed onto the implants, and standardized metal crowns (maxillary central incisors) were cemented and subjected to step-stress accelerated life testing in water. Use-level probability Weibull curves and reliability for 100,000 cycles at 150 and 200 N (90% two-sided confidence intervals) were calculated. Polarized light and scanning electron microscopes were used to access the failure modes. Results: The calculated survival probability for 100,000 cycles at 150 N was approximately 93% in group ASC, 98% in group BSC, 94% in group ISC, and 99% in group IMC. At 200 N, the survival rate was estimated to be approximately < 0.1% for ASC, 77% for BSC, 34% for ISC, and 93% for IMC. Abutment screw fracture was the main failure mode for all groups. Conclusions: Although the probability of survival was not significantly different among systems at a load of 150 N, a significant decrease was observed at 200 N for all groups except IMC.

Influence of abutment-to-fixture design on reliability and failure mode of all-ceramic crown systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise estatística da vida em fadiga de compósitos estruturais pela distribuição de Weibull

Nowadays technological trend is based on finding materials that could support low weight with satisfactory mechanical properties and for this reason composite material became a very attractive topic in research projects all over the world. Due to its heterogenic properties, this type of material shows scatter in mechanical test results, especially in cyclic loading. Therefore it is important to predict its fatigue strength behaviour by statistic analysis, once fatigue causes approximately 90% of the failure in structural components. The present work aimed to investigate the fatigue behaviour of the Twill/Cycom 890 composite, which is carbon fiber reinforced with polymeric resin as matrix and manufactured via RTM process (Resin Transfer Molding). All samples were tested in different tensile level in triplicate in order to associate these values. The statistical analysis was conducted with Two-Parameter Weibull Distribution and then evaluated the fatigue life results for the composite. Weibull graphics were used to determine the scale and shape parameters. The S-N curve for the Twill/Cycom composite was drawn and indicated the number of cycles to occur the first damages in this material. The probability of failure was associated with material reliability, as shown in graphics for the different tensile levels and fatigue life. In addition, the laminate was evaluated by ultrasonic inspection showing a regular impregnation. The fractographic analysis conducted by SEM showed failure mechanisms for polymeric composites associated to cyclic loadings ... (Complete abstract click electronic access below)

The cross-entropy method for network reliability estimation

Consider a network of unreliable links, modelling for example a communication network. Estimating the reliability of the network-expressed as the probability that certain nodes in the network are connected-is a computationally difficult task. In this paper we study how the Cross-Entropy method can be used to obtain more efficient network reliability estimation procedures. Three techniques of estimation are considered: Crude Monte Carlo and the more sophisticated Permutation Monte Carlo and Merge Process. We show that the Cross-Entropy method yields a speed-up over all three techniques.

Traffic Offered Behaviour Regarding Target QOS Parameters in Network Dimensioning

We consider a model of overall telecommunication network with virtual circuits switching, in stationary state, with Poisson input flow, repeated calls, limited number of homogeneous terminals and 8 types of losses. One of the main problems of network dimensioning/redimensioning is estimation of traffic offered in network because it reflects on finding of necessary number of circuit switching lines on the basis of the consideration of detailed users manners and target Quality of Service (QoS). In this paper we investigate the behaviour of the traffic offered in a network regarding QoS variables: “probability of blocked switching” and “probability of finding B-terminals busy”. Numerical dependencies are shown graphically. A network dimensioning task (NDT) is formulated, solvability of the NDT and the necessary conditions for analytical solution are researched as well. International Journal "Information Technologies and Knowledge" Vol.2 / 2008 174 The received results make the network dimensioning/redimensioning, based on QoS requirements easily, due to clearer understanding of important variables behaviour. The described approach is applicable directly for every (virtual) circuit switching telecommunication system e.g. GSM, PSTN, ISDN and BISDN. For packet - switching networks, at various layers, proposed approach may be used as a comparison basis and when they work in circuit switching mode (e.g. VoIP).

The reliability mathematical model of load-sharing parallel systems based on fatigue cumulative damage

Conventional reliability models for parallel systems are not applicable for the analysis of parallel systems with load transfer and sharing. In this short communication, firstly, the dependent failures of parallel systems are analyzed, and the reliability model of load-sharing parallel system is presented based on Miner cumulative damage theory and the full probability formula. Secondly, the parallel system reliability is calculated by Monte Carlo simulation when the component life follows the Weibull distribution. The research result shows that the proposed reliability mathematical model could analyze and evaluate the reliability of parallel systems in the presence of load transfer.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Coordinated probabilistic optimal decision-making model for multi-area ATC with risk control

In the decision-making of multi-area ATC (Available Transfer Capacity) in electricity market environment, the existing resources of transmission network should be optimally dispatched and coordinately employed on the premise that the secure system operation is maintained and risk associated is controllable. The non-sequential Monte Carlo simulation is used to determine the ATC probability density distribution of specified areas under the influence of several uncertainty factors, based on which, a coordinated probabilistic optimal decision-making model with the maximal risk benefit as its objective is developed for multi-area ATC. The NSGA-II is applied to calculate the ATC of each area, which considers the risk cost caused by relevant uncertainty factors and the synchronous coordination among areas. The essential characteristics of the developed model and the employed algorithm are illustrated by the example of IEEE 118-bus test system. Simulative result shows that, the risk of multi-area ATC decision-making is influenced by the uncertainties in power system operation and the relative importance degrees of different areas.

Delay tolerant routing protocols for energy-neutral animal tracking

This paper investigates communication protocols for relaying sensor data from animal tracking applications back to base stations. While Delay Tolerant Networks (DTNs) are well suited to such challenging environments, most existing protocols do not consider the available energy that is particularly important when tracking devices can harvest energy. This limits both the network lifetime and delivery probability in energy-constrained applications to the point when routing performance becomes worse than using no routing at all. Our work shows that substantial improvement in data yields can be achieved through simple yet efficient energy-aware strategies. Conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination. We use empirical traces collected in a flying fox (fruit bat) tracking project and show that simple threshold-based energy-aware strategies yield up to 20% higher delivery rates. Furthermore, these results generalize well for a wide range of operating conditions.

Energy–aware forwarding strategies for delay tolerant networking (DTN) routing protocols

This research addresses efficient use of the available energy in resource constrained mobile sensor nodes to prevent early depletion of the battery and maximize the packet delivery rate. This research contributes two energy-aware enhancement strategies to improve the network lifetime and delivery probability for energy constrained applications in the delay-tolerant networking environment.