Simulação do sistema de esgotamento sanitário de Ponta Negra- Natal: mitigação dos riscos ambientais e estratégia de manutenção preditiva

The simulation of SES-Natal Ponta Negra: mitigation of environmental risks and predictive maintenance strategy was developed in the context of several operational irregularities in the pumping stations and sewage systems in the system Ponta Negra. Thus, the environmental risks and complaints against the company due to overflows of sewage into the public thoroughfare became common. This neighborhood has shown in recent years an increase of resident higher than the initial expectation of growth. In this sense presumed the large population growth and generation of sewers higher than expected, associated to the use of corrective maintenance and misuse of the system may be the main causes of operational failures occurring in the SES. This study aimed at analyzing the hydraulic behavior of SES Ponta Negrathrough numerical simulation of its operation associated to future scenarios of occupation. The SES Ponta Negra has a long lengthof collection networks and 6 pumping stations interconnected, being EE 1, 2, 4 coastal way, and beach Shopping interconnected EE3 to receives all sewers pumped from the rest pumping station and pumped to the sewage treatment station of neighborhood which consists of a facultative pond followed by three maturation ponds with disposal of treated effluent into infiltration ditches. Oncethey are connected with each other, the study was conducted considering the days and times of higher inflow for all lifts. Furthermore, with the aim of measuring the gatherer network failures were conducted data survey of on the networks. Thephysical and operational survey data was conducted between January/2011 and janeiro/2012. The simulation of the SES was developed with the aim ofdemonstrating its functioning, eithercurrently and in the coming years, based in population estimates and sewage flow. The collected data represents the current framework of the pumping stations of the SES Ponta Negra and served as input to the model developed in MS Excel ® spreadsheet which allowed simulating the behavior of SES in future scenarios. The results of this study show thatBeach Shopping Pumping Station is actually undersized and presents serious functioning problemsthatmay compromise the environmental quality of surrounding area. The other pumping stations of the system will reach itsmaximum capacity between 2013 and 2015, although the EE1 and EE3 demonstrateoperation capacity, even precariously, until 2017. Moreover, it was observed that the misuse of the network system, due to the input of both garbage and stormwater, are major factors of failures that occur in the SES. Finally, it was found that the corrective maintenance appliance, rather than predictive,has proven to beinefficient because of the serious failuresin the system, causing damage to the environment and health risks to users

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Predictive Maintenance for Improved Sustainability - An Ion Beam Etch Endpoint Detection System Use Case

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.

Design of a labVIEW system applied to predictive maintenance

The implementation of vibration analysis techniques based on virtual instrumentation has spread increasingly in the academic and industrial branch, since the use of any software for this type of analysis brings good results at low cost. Among the existing software for programming and creation of virtual instruments, the LabVIEW was chosen for this project. This software has good interface with the method of graphical programming. In this project, it was developed a system of rotating machine condition monitoring. This monitoring system is applied in a test stand, simulating large scale applications, such as in hydroelectric, nuclear and oil exploration companies. It was initially used a test stand, where an instrumentation for data acquisition was inserted, composed of accelerometers and inductive proximity sensors. The data collection system was structured on the basis of an NI 6008 A/D converter of National Instruments. An electronic circuit command was developed through the A/D converter for a remote firing of the test stand. The equipment monitoring is performed through the data collected from the sensors. The vibration signals collected by accelerometers are processed in the time domain and frequency. Also, proximity probes were used for the axis orbit evaluation and an inductive sensor for the rotation and trigger measurement. © (2013) Trans Tech Publications, Switzerland.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Predictive maintenance of a worm reducer with an unbalanced load

Among all predictive maintenance techniques the oil analysis and vibration analysis are the most important for monitoring some mechanical systems. The integration of these techniques has potential to improve industrial maintenance practices and provide a better economic gain for industries. To study the integration of these two techniques, a test rig was set up to obtain an extreme working condition for the worm reducer used in this paper. The test rig was composed by a motor connected to a reducer through a flexible coupling and with an unbalanced load. The analysis of the results carried out by using a sample of the oil recommended by the manufacturer in extreme conditions, and using liquid contaminant is presented. From the results it was observed that if there is an abnormal instantaneous load in a system, the subsequent vibration analysis may not perceive what occurred if there was no permanent damage, which is not the case with the lubricant analysis.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

Development of a Solar Assisted Heat Pump Desalination System

In view of the growing global demand for energy and concern expressed for environmental degradation, a clean and "free" energy source, such as solar energy, has been receiving greater attention in recent years for various applications using different techniques. The Direct Expansion Solar Assisted Heat Pump (DX-SAHP) principle is one of the most promising techniques as it makes use of both solar and ambient energy. As the system has capability to function at low temperatures, it has the potential to operate at night in the tropics. The system utilizes multi-effect distillation (MED) principle for the conversion of seawater to fresh water. An experimental setup of the DX-SAHP desalination system has been built at the Department of Mechanical Engineering, National University of Singapore (NUS). This system uses two types of flat-plate solar collectors. One is called evaporator-collector, where no glazing is used, and the efficiency varies between 80 and 90%. The other type of collector is single-glazed, where the maximum efficiency is about 60%, and it is used for feed water heating. For the heat pump cycle, refrigerant R134a is used. The present study provides a comprehensive analyses and performance evaluation of this system under different operating and meteorological conditions of Singapore. The Coefficient of Performance (COP) of the heat pump system reached a maximum value of 10. For a single effect of desalination, the system shows a Performance Ratio (PR) of around 1.3.

Risk based lifetime costs assessment of a ground source heat pump (GSHP) system design: Methodology and case study

Space heating accounts for a large portion of the world's carbon dioxide emissions. Ground Source Heat Pumps (GSHPs) are a technology which can reduce carbon emissions from heating and cooling. GSHP system performance is however highly sensitive to deviation from design values of the actual annual energy extraction/rejection rates from/to the ground. In order to prevent failure and/or performance deterioration of GSHP systems it is possible to incorporate a safety factor in the design of the GSHP by over-sizing the ground heat exchanger (GHE). A methodology to evaluate the financial risk involved in over-sizing the GHE is proposed is this paper. A probability based approach is used to evaluate the economic feasibility of a hypothetical full-size GSHP system as compared to four alternative Heating Ventilation and Air Conditioning (HVAC) system configurations. The model of the GSHP system is developed in the TRNSYS energy simulation platform and calibrated with data from an actual hybrid GSHP system installed in the Department of Earth Science, University of Oxford, UK. Results of the analysis show that potential savings from a full-size GSHP system largely depend on projected HVAC system efficiencies and gas and electricity prices. Results of the risk analysis also suggest that a full-size GSHP with auxiliary back up is potentially the most economical system configuration. © 2012 Elsevier Ltd.

A framework for environmental management system adoption and maintenance : an Australian perspective

Purpose – The purpose of this paper is to present a framework for environmental management system (EMS) adoption which can be used by managers to enable them to achieve a faster, smoother, effective and sustainable EMS implementation. This is termed by the authors as “successful” EMS implementation.
Design/methodology/approach – The framework presented is based on an extensive field study conducted in Australia that included a questionnaire survey and in-depth interviews conducted with practitioners, the results of which have been reported in this and other journals.
Findings – The framework presented covers three distinct phases, namely development, certification and maintenance and continuous improvement. Preliminary assessment of this framework by a group of Australian experts that included four senior managers, one academic and one consultant suggests that this framework is a useful tool for implementing an effective EMS.
Practical implications – Each of the three phases of the framework provides a step-by-step approach and a sequential map towards a successful EMS implementation.
Originality/value – The framework has been developed from extensive fieldwork and has been validated by a group of “experts” which comprised of four industry practitioners, one management academic, and one EMS consultant. The framework provides a detailed understanding of the steps involved in the implementation, certification, and maintenance of EMS.

The Role of Implant/Abutment System on Torque Maintenance of Retention Screws and Vertical Misfit of Implant-Supported Crowns Before and After Mechanical Cycling

Purpose: This study aimed to evaluate the role of the implant/abutment system on torque maintenance of titanium retention screws and the vertical misfit of screw-retained implant-supported crowns before and after mechanical cycling. Materials and Methods: Three groups were studied: morse taper implants with conical abutments (MTC group), external-hexagon implants with conical abutments (EHC group), and external-hexagon implants with UCLA abutments (EHU group). Metallic crowns casted in cobalt-chromium alloy were used (n = 10). Retention screws received insertion torque and, after 3 minutes, initial detorque was measured. Crowns were retightened and submitted to cyclic loading testing under oblique loading (30 degrees) of 130 +/- 10 N at 2 Hz of frequency, totaling 1 x 10(6) cycles. After cycling, final detorque was measured. Vertical misfit was measured using a stereomicroscope. Data were analyzed by analysis of variance, Tukey test, and Pearson correlation test (P < .05). Results: All detorque values were lower than the insertion torque both before and after mechanical cycling. No statistically significant difference was observed among groups before mechanical cycling. After mechanical cycling, a statistically significantly lower loss of detorque was verified in the MTC group in comparison to the EHC group. Significantly lower vertical misfit values were noted after mechanical cycling but there was no difference among groups. There was no significant correlation between detorque values and vertical misfit. Conclusions: All groups presented a significant decrease of torque before and after mechanical cycling. The morse taper connection promoted the highest torque maintenance. Mechanical cycling reduced the vertical misfit of all groups, although no significant correlation between vertical misfit and torque loss was found.

A green fluorescent protein-reporter mammalian two-hybrid system with extrachromosomal maintenance of a prey expression plasmid: Application to interaction screening

An improved mammalian two-hybrid system designed for interaction trap screening is described in this paper. CV-1/EBNA-1 monkey kidney epithelial cells expressing Epstein–Barr virus nuclear antigen 1 (EBNA-1) were stably transfected with a reporter plasmid for GAL4-dependent expression of the green fluorescent protein (GFP). A resulting clone, GB133, expressed GFP strongly when transfected transiently with transcriptional activators fused to GAL4 DNA-binding domain with minimal background GFP expression. GB133 cells maintained plasmids containing the OriP Epstein–Barr virus replication origin that directs replication of plasmids in mammalian cells in the presence of the EBNA-1 protein. GB133 cells transfected stably with a model bait expressed GFP when further transfected transiently with an expression plasmid for a known positive prey. When the bait-expressing GB133 cells were transfected transiently with an OriP-containing expression plasmid for the positive prey together with excess amounts of empty vector, cells that received the positive prey were readily identified by green fluorescence in cell culture and eventually formed green fluorescent microcolonies, because the prey plasmid was maintained by the EBNA-1/Ori-P system. The green fluorescent microcolonies were harvested directly from the culture dishes under a fluorescence microscope, and total DNA was then prepared. Prey-encoding cDNA was recovered by PCR using primers annealing to the vector sequences flanking the insert-cloning site. This system should be useful in mammalian cells for efficient screening of cDNA libraries by two-hybrid interaction.

Report on Wood River Drainage and Levee District, Madison County, Illinois : Grassy Lake Pump Station /

A recommendation to use a portion of the Illinois General Assembly's appropriation for the Wood River Drainage and Levee District--funds to help defray the District's funding requirements associated with U.S. Army Corps of Engineers' projects--for the construction of the Grassy Lake Pump Station to alleviate interior flooding within the District.