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

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.

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.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

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.

Designing a support system for aerospace maintenance supply chains

Purpose - To develop a systems strategy for supply chain management in aerospace maintenance, repair and overhaul (MRO). Design/methodology/approach - A standard systems development methodology has been followed to produce a process model (i.e. the AMSCR model); an information model (i.e. business rules) and a computerised information management capability (i.e. automated optimisation). Findings - The proof of concept for this web-based MRO supply chain system has been established through collaboration with a sample of the different types of supply chain members. The proven benefits comprise new potential to minimise the stock holding costs of the whole supply chain whilst also minimising non-flying time of the aircraft that the supply chain supports. Research limitations/implications - The scale of change needed to successfully model and automate the supply chain is vast. This research is a limited-scale experiment intended to show the power of process analysis and automation, coupled with strategic use of management science techniques, to derive tangible business benefit. Practical implications - This type of system is now vital in an industry that has continuously decreasing profit margins; which in turn means pressure to reduce servicing times and increase the mean time between them. Originality/value - Original work has been conducted at several levels: process, information and automation. The proof-of-concept system has been applied to an aircraft MRO supply chain. This is an area of research that has been neglected, and as a result is not well served by current systems solutions. © Emerald Group Publishing Limited.

S332 pump station, south of TS/Ph-1, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Utilização de gás natural comprimido em veículos pesados de passageiros

Dissertação de mestrado em Engenharia Mecânica

Mobile system for pump working point estimation

In the current economy situation companies try to reduce their expenses. One of the solutions is to improve the energy efficiency of the processes. It is known that the energy consumption of pumping applications range from 20 up to 50% of the energy usage in the certain industrial plants operations. Some studies have shown that 30% to 50% of energy consumed by pump systems could be saved by changing the pump or the flow control method. The aim of this thesis is to create a mobile measurement system that can calculate a working point position of a pump drive. This information can be used to determine the efficiency of the pump drive operation and to develop a solution to bring pump’s efficiency to a maximum possible value. This can allow a great reduction in the pump drive’s life cycle cost. In the first part of the thesis, a brief introduction in the details of pump drive operation is given. Methods that can be used in the project are presented. Later, the review of available platforms for the project implementation is given. In the second part of the thesis, components of the project are presented. Detailed description for each created component is given. Finally, results of laboratory tests are presented. Acquired results are compared and analyzed. In addition, the operation of created system is analyzed and suggestions for the future development are given.

Monitoring of centrifugal pump operation by a frequency converter

Centrifugal pumps are widely used in industrial and municipal applications, and they are an important end-use application of electric energy. However, in many cases centrifugal pumps operate with a significantly lower energy efficiency than they actually could, which typically has an increasing effect on the pump energy consumption and the resulting energy costs. Typical reasons for this are the incorrect dimensioning of the pumping system components and inefficiency of the applied pump control method. Besides the increase in energy costs, an inefficient operation may increase the risk of a pump failure and thereby the maintenance costs. In the worst case, a pump failure may lead to a process shutdown accruing additional costs. Nowadays, centrifugal pumps are often controlled by adjusting their rotational speed, which affects the resulting flow rate and output pressure of the pumped fluid. Typically, the speed control is realised with a frequency converter that allows the control of the rotational speed of an induction motor. Since a frequency converter can estimate the motor rotational speed and shaft torque without external measurement sensors on the motor shaft, it also allows the development and use of sensorless methods for the estimation of the pump operation. Still today, the monitoring of pump operation is based on additional measurements and visual check-ups, which may not be applicable to determine the energy efficiency of the pump operation. This doctoral thesis concentrates on the methods that allow the use of a frequency converter as a monitoring and analysis device for a centrifugal pump. Firstly, the determination of energy-efficiency- and reliability-based limits for the recommendable operating region of a variable-speed-driven centrifugal pump is discussed with a case study for the laboratory pumping system. Then, three model-based estimation methods for the pump operating location are studied, and their accuracy is determined by laboratory tests. In addition, a novel method to detect the occurrence of cavitation or flow recirculation in a centrifugal pump by a frequency converter is introduced. Its sensitivity compared with known cavitation detection methods is evaluated, and its applicability is verified by laboratory measurements for three different pumps and by using two different frequency converters. The main focus of this thesis is on the radial flow end-suction centrifugal pumps, but the studied methods can also be feasible with mixed and axial flow centrifugal pumps, if allowed by their characteristics.