Friction Torque and Leakage Based Data-Driven Approach for Rotary Seal Prognostics in Manufacturing Industry

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Manufacturing Industry ◽  
Friction Torque ◽  
Operating Conditions ◽  
Oil Field ◽  
Data Driven ◽  
Sensor Technology ◽  
Physical Parameters ◽  
Direct Estimation ◽  
Rotary Seal ◽  
Wide Range

Abstract Rotary seals are found in many manufacturing equipment and machines used for various applications under a wide range of operating conditions. Rotary seal failure can be catastrophic and can lead to costly downtime and large expenses; so it is extremely important to assess the degradation of rotary seal to avoid fatal breakdown of machineries. Physics-based rotary seal prognostics require direct estimation of different physical parameters to assess the degradation of seals. Data-driven prognostics utilizing sensor technology and computational capabilities can aid in the in-direct estimation of rotary seals’ running condition unlike the physics-based approach. An important aspect of data-driven prognostics is to collect appropriate data in order to reduce the cost and time associated with the data collection, storage and computation. Seals in machineries operate in harsh conditions, especially in the oil field, seals are exposed to harsh environment and aggressive fluids which gradually reduces the elastic modulus and hardness of seals, resulting in lower friction torque and excessive leakage. Therefore, in this study we implement a data-driven prognostics approach which utilizes friction torque and leakage signals along with Multilayer Perceptron as a classifier to compare the performance of the two metrics in classifying the running condition of rotary seals. Friction torque was found to have a better performance than leakage in terms of differentiating the running condition of rotary seals throughout its service life. Although this approach was designed for seals in oil and gas industry, this approach can be implemented in any manufacturing industry with similar applications.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Accelerated Aging ◽  
Testing Procedure ◽  
Health Condition ◽  
Performance Degradation ◽  
Friction Torque ◽  
Data Driven ◽  
Rotary Seal ◽  
Unseen Data

Failure of the rotary seal is one of the foremost causes of breakdown in rotary machinery, and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessing the performance degradation of the rotary seal is very important for maintenance decision-making. Although significant progress has been made over the last 5 years to understand the degradation of seals using experimental verification and numerical simulation, there is a research gap on the data-driven-based tools and methods to assess the health condition of rotary seals. In this paper, we propose a data-driven-based performance degradation assessment approach to classify the running/health condition of rotary seals, which was not considered in the previous studies. Statistical time domain features are extracted from friction torque-based degradation signals collected from a rotary setup. Wrapper-based feature selection approach is used to select relevant features, with multilayer perceptron neural network utilized as a classification technique. To validate the proposed methodology, an accelerated aging and testing procedure is developed to capture the performance of rotary seals. The study findings indicate that multilayer perceptron (MLP) classifier built using features related to the amplitude of torque signal has a better classification accuracy for unseen data when compared with logistic regression and random forest classifiers.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2020 ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

China Rising

2015 ◽  
Vol 42 (6) ◽  
pp. 88-104 ◽  
Author(s):  
Marcelo Curado
Keyword(s):  
Manufacturing Industry ◽  
Job Creation ◽  
Radical Change ◽  
The State ◽  
Operating Conditions ◽  
Capitalist System ◽  
Wide Range ◽  
Productive Structure

China’s economic rise has caused a radical change in the operating conditions of the capitalist system and opened a wide range of opportunities and challenges for Brazil’s development. The Brazilian productive structure is going through great changes, key among them being the loss of importance of the manufacturing industry in job creation and in total GDP, and the efforts of Brazilian capitalists to promote investment and innovation have fallen short. The latter is a key element for an understanding of Brazilian industry’s weak participation in the highly competitive international scene. These opportunities and challenges can only be adequately addressed by the maintenance and expansion of the planning efforts of the state.A ascensão econômica da China tem causado mudança radical nas condições de fun-cionamento do sistema capitalista e criou um vasto espectro de oportunidades e desafios ao desenvolvimento do Brasil. A estrutura produtiva brasileira vem passando por grandes transformações. Entre elas, uma das mais sérias tem sido a perda na capacidade da indústria manufatureira de gerar empregos e a queda de sua participação no PIB. Demais, nota-se que os esforços de capitalistas brasileiros para promover investimento e inovação têm sido pífios. Esse último problema constitui elemento-chave para compreender-se a participação fraca da indústria brasileira em um cenário international extremamente competitivo. As oportunidades e desafios mencionados só podem ser adequadamente tratados por meio da manutenção e expansão do planejamento estatal.

scholarly journals Numerical Simulation of a Slipper Model with Multi-Lands and Grooves for Hydraulic Piston Pumps and Motors in Mixed Lubrication

Lubricants ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 55
Author(s):  
Toshiharu Kazama
Keyword(s):  
Numerical Simulation ◽  
Contact Pressure ◽  
Power Loss ◽  
Numerical Solutions ◽  
Friction Torque ◽  
Mixed Lubrication ◽  
Operating Conditions ◽  
Axial Piston Pumps ◽  
Wide Range ◽  
Axial Piston

A theoretical model of a slipper with multi-lands and multi-grooves for swashplate type axial piston pumps and motors was established, including surface interactions. Further, a numerical simulation was conducted under an unsteady state and mixed lubrication conditions. Four model configurations were considered: A slipper with a single main land; a slipper with inner and main lands and a groove; a slipper with outer and main lands and a groove; and a slipper with inner, main, and outer lands with two grooves. Numerical solutions were obtained across a wide range of operating conditions in terms of center clearance, pad attitude, contact pressure, flow rate, friction torque, power loss, and stiffness. The motion and characteristics were differentiated into two groups: Slippers with a single-land and an annex inner-land; and slippers with an annex outer-land and a triple-land. The single-land and annex inner-land slippers exhibited smaller pad swing, whereas the triple-land and annex outer-land slippers reduced contact pressure and power loss.

scholarly journals Standards for Cyber-Physical Energy Systems—Two Case Studies from Sensor Technology

2019 ◽  
Vol 9 (3) ◽  
pp. 435 ◽  
Author(s):  
Michael Krutwig ◽  
Bernhard Kölmel ◽  
Adrian Tantau ◽  
Kejo Starosta
Keyword(s):  
Case Studies ◽  
Smart Grids ◽  
Manufacturing Industry ◽  
Energy Systems ◽  
Sensor Technology ◽  
Area Network ◽  
Wide Area Network ◽  
Physical Energy ◽  
Wide Range ◽  
Operational Energy

Cyber-physical energy systems (CPES) describe a specialization of the cyber-physical system concept, in which energy systems are transformed into intelligent energy networks. These systems provide the basis for the realization of smart microgrids and smart grids. In the last decade, numerous research projects have intensively explored the fundamentals and modeling of CPES and validated them in pilot projects. In the meantime, more and more CPES solutions have been appearing on the market and the battle for the most suitable standards has begun. This paper gives an overview of the currently available standards for CPES sensor technologies and assesses the suitability for implementation. In two case studies in the application area of operational energy management in German companies, a sensor retrofitting is described—once with proprietary technology and once using the standards Long Range (LoRa) Wide Area Network and OPC Unified Architecture (OPC UA). As a result, the shortcomings of the standards for their use in CPES are shown and discussed. OPC UA, which was originally developed for the manufacturing industry, turns out to be to be a suitable standard for a wide range of CPES implementations.

Contactless Superconducting Magnetic Instrument for Precise Positioning in Cryogenic Environments

2013 ◽  
Author(s):  
J. L. Perez-Diaz ◽  
I. Valiente-Blanco ◽  
E. Diez-Jimenez ◽  
J. Sanchez-Garcia-Casarrubios ◽  
M. A. Alvarez-Valenzuela ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Magnetic Levitation ◽  
High Vacuum ◽  
Far Infrared ◽  
Operating Conditions ◽  
Guidance System ◽  
Small Scale ◽  
Large Area ◽  
Precision Engineering ◽  
Wide Range

There is an increasing demand of nanotechnology and nano-devices in microelectronics, optics, biomedical and precision engineering industries. In this context, a wide range of applications require micrometer/nanometer positioning within a long range. Ultra precision manufacturing and inspection systems in micro-automating semiconductor fabrication, nanopositioning and nanomeasuring machines (NPM-Machine), development of MEMS and NEMS, copying machines, stepper stages for photolithography, small-scale measuring machines (CMMs) for large area scanning or surface imaging in scanning probe microscopy (SPM) are a few examples of these applications. In some applications, cryogenic environments (temperatures below 120 K) are a desirable or mandatory condition. The sensitivity of a large number of sensors is greatly increased when they are at cryogenics temperatures, like for example, those required for far infrared interferometer spectroscopy. The operating conditions in these environments include very low temperatures but also high vacuum. In this context, it is challenging for mechanisms to overcome all the tribological problems associated with these conditions. In addition very low energy consumption is also desirable in cryogenic environments. The invention here presented is a contactless linear slider that gets benefit of superconducting magnetic levitation to obtain a nanometer resolution within a long stroke (∼ 15 mm), minimizing run-outs of the slider (in the micron scale). Moreover, due to self-stable levitation and guidance of the slider, the complexity of the control is significantly reduced and the power consumption minimized (of the order of mW). The linear slider can be divided in two subsystems: the guidance system and the actuating system. The guidance system is composed of a static guideline, made of two superconducting disks and a slider composed of a long permanent magnet. Due to the high translational symmetry of the magnetic field generated by the PM, a contactless sliding kinematic pair is established between the PM and the superconductors in the sliding DoF. Thus, the slider is able to be moved in the sliding direction with very low resistance. However, greater restoring forces appear if the PM is moved in any other direction. Due to the lack of contact between the moving parts is also suitable for operation in clean-room applications, like in semiconductor manufacturing industry. Ultimately, the device was designed, built and tested in a relevant cryogenic environment (15 K and high vacuum) and the results introduced and discussed.

Multi-Objective Optimal Airfoil Design for Cargo Aircrafts

2016 ◽  
Author(s):  
Yousef Naranjani ◽  
Jian-Qiao Sun
Keyword(s):  
Design Procedure ◽  
Aircraft Design ◽  
Processing Technique ◽  
Rate Of Change ◽  
Operating Conditions ◽  
Solution Set ◽  
Physical Parameters ◽  
Objective Functions ◽  
Multi Objective ◽  
Wide Range

The airfoil/wing design is probably the most important part of an aircraft design. A practical aerodynamic design of airfoil requires optimal performance on a wide range of operating conditions. These requirements are often found to be conflicting and demand designer expertise for satisfactory results, not to mention the computational burden of the simulations. Although there exists many studies on direct and inverse design of airfoils, less attention has been paid to simultaneous consideration of multiple objectives. In this paper, a multi-objective optimal airfoil design procedure is presented. PARSEC parametrization method has been utilized to express the airfoil geometry in terms of twelve physical parameters. The aerodynamic performance is obtained by 2D panel method using XFOIL package. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been applied for airfoil geometry design because it is efficient and keeps the diversity among the solution set. The objective functions and constraints are chosen to enhance the flight performance at takeoff, cruise, and landing conditions for a long range cargo aircraft. Objectives include maximization of lift to drag ratio (CL/CD), maximization of rate of change of lift to attack angle (dCL/dα) for having increased lift at takeoff/landing condition and minimization of pitching moment CM2. Two applied constraints are CL > CLmin at operating condition and thickness ≤ %25. Each evaluation is consist of finding the optimal operating angle of attack and reporting the corresponding objective values. The quality of the solution at various generations has been studied to guarantee the convergence of the solution. Like any other multi-objective optimization problem (MOP), the solution would be a set of Pareto optimal configurations. Although having multiple solutions gives us a better understanding of the problem, only one configuration should be chosen by the designer. A post processing technique is also used to help the decision maker to choose the most appropriate compromise in the solution set. The method is found to be effective in finding efficient set of airfoils. The simulation is also found to be effective because it can be done on a regular personal computer. It should be noticed that the method can be easily applied to other airfoil design applications by simply modifying the objective functions and the constraints.

Very Low Concentration Sand Transport in Multiphase Horizontal Pipelines: An Experimental Study and Modeling Guidelines

2015 ◽  
Author(s):  
Kamyar Najmi ◽  
Brenton S. McLaury ◽  
Siamack A. Shirazi ◽  
Selen Cremaschi
Keyword(s):  
Experimental Data ◽  
Particle Interaction ◽  
Operating Conditions ◽  
Sand Transport ◽  
Physical Parameters ◽  
Volume Percent ◽  
Flow Rates ◽  
Low Concentration ◽  
Horizontal Pipes ◽  
Wide Range

Very low concentration sand transport in multiphase horizontal pipes is experimentally investigated in this study. Sand concentration is chosen to be low enough to ignore the effect of particle-particle interaction. This is done to obtain the liquid and gas threshold flow rates which are required to move particles at low concentration (0.002 volume percent) of particles in multiphase pipelines. Along with obtaining the threshold flow rates, the effects of sand concentration, sand size, pipe size and liquid viscosity are also experimentally investigated. Critical velocity is defined to make sure all grains of sand continuously move in the pipe. The experimental data were obtained for a wide range of operating conditions in both intermittent and stratified flow regimes. A comparison of the obtained experimental data in this study with similar studies in the literature reveals the effect of some physical parameters affecting sad transport in multiphase flow pipelines. This study also gives some general guidelines for a more accurate model to predict minimum flow rates to move sand in multiphase flows.

scholarly journals Data-driven nonlinear aeroelastic models of morphing wings for control

2020 ◽  
Vol 476 (2239) ◽  
pp. 20200079
Author(s):  
N. Fonzi ◽  
S. L. Brunton ◽  
U. Fasel
Keyword(s):  
Operating Conditions ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
Next Generation ◽  
Morphing Wing ◽  
Aeroelastic System ◽  
Aerospace Structures ◽  
Wide Range ◽  
Operating Regimes

Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modelling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: (1) we introduce a formulation to handle algebraic equations, and (2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modelling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modelling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies.

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