Investigation in modeling a load-sensing pump using dynamic neural unit based dynamic neural networks

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of “black box” approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the “black box” approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the “pump only” model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a “pump only” model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the “error accumulation” problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response

Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels

El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida. Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje empleados. Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las soluciones computacionales y de simulación se centran principalmente en la predicción off-line de vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros. Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales. En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso. Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process has been used since 1950s in the aerospace industry despite its environmental concern. Among its advantages, chemical milling does not induce residual stress and parts meet the required tolerances. However, this process is a pollutant and costly technology. Thanks to the last advances in conventional milling, machining processes can achieve similar quality results meanwhile vibration and part deflection are avoided. Both problems are usually related to the cutting parameters and the workholding. This thesis analyses the causes of the cutting instability and part deformation through a literature review that covers analytical models, computational techniques and industrial solutions. Analytics and computational solutions are mainly focused on chatter and deflection prediction and industrial approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening devices, adaptive control systems based on simulations and the statistical parameters selection, and CAM solutions combined with the use of virtual twins applications. In this literature review, few research works about thin-plates and thin-floors is found so the effect of the cutting parameters is also studied experimentally. These experiments confirm that even using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining. On the one hand, roughness values meet the required tolerances under every set of the tested parameters. On the other hand, a proper parameter selection reduces the cutting forces and process tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify workholding and increase the machine efficiency. Another way to improve the process efficiency is to increase tool life by using cutting fluids. Their use can also decrease the temperature of the process and the induced stresses. For this purpose, different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of the ILs is used, which, therefore, could considerably increase tool life

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Optimization of a retrofit urea-SCR system

Oxides of nitrogen (NOx) emissions from legacy diesel engines are often many times over currently mandated standards, contributing significantly to degradation of air quality and negative environmental impact. A retrofit urea-SCR (selective catalytic reduction) aftertreatment system offers a viable solution for reducing NOx emissions from older diesel engines. A stand-alone retrofit urea-SCR aftertreatment system was previously developed by West Virignia University (WVU) engineers, implementing a pre-SCR NOx sensor, open-loop feed-forward control, and stoichiometric NOx reduction logic. During experimental testing at WVU, the urea-SCR system demonstrated NOx reductions of 2% to 53%, depending on the test cycle. In order to optimize the system, this dissertation considered additional control configurations. To evaluate the emissions performance of each control strategy, a neural network heavy-duty diesel engine model was developed along with separate four-state chemical and thermal SCR catalyst models. Each model component was validated with experimental data recorded from the WVU Engine and Emissions Research Laboratory (EERL). The following control configurations were considered: (1) pre-SCR NOx sensor, open-loop feed-forward control, (2) post-SCR NOx sensor, closed-loop feed-back proportional-derivative (PD) control, (3) pre- and post-SCR NOx sensors, closed-loop feed-back proportional-integral-derivative (PID) control, (4) pre-SCR NOx sensor, model-based control.;The evaluation process considered differences between a highly instrumented and highly engineered system. Emissions performance was evaluated over two transient on-road test cycles (FTP, ACES HHDDT_S) and one steady-state marine test cycle (ICOMIA E5), implying broad applicability of the aftertreatment system. The evaluation process was characterized by overall NOx reduction percentage, maximum ammonia slip in parts per million (ppm), and average ammonia slip (ppm). The complexity of the sensor configuration and control strategy calibration were evaluated, as well as how adaptable a given configuration was to variations in engine behavior and sensor measurement accuracy. Finally, total cost was compared between each control configuration, considering system capital, maintenance, operation, control strategy engineering, and system calibration. A final cost per ton of NOx reduced was presented for each control configuration, assuming a six year operational cycle in marine and on-road applications. Based on the collective emissions, complexity, and cost analyses, a configuration implementing pre- and post-SCR NOx sensors and closed-loop PID control was identified as optimal for a retrofit application. Model results demonstrated NOx reductions of 44%, 53%, and 47% over FTP, ICOMIA, and ACES High-Speed Cruise (HHDDT_S) cycles, respectively. The total annual NOx reduction cost was {dollar}8,800 per ton of NOx reduced for an on-road application and {dollar}3,651 per ton of NOx reduced for a marine application

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme

Advanced control designs for output tracking of hydrostatic transmissions

The work addresses simple but efficient model descriptions in a combination with advanced control and estimation approaches to achieve an accurate tracking of the desired trajectories. The proposed control designs are capable of fully exploiting the wide operation range of HSTs within the system configuration limits. A new trajectory planning scheme for the output tracking that uses both the primary and secondary control inputs was developed. Simple models or even purely data-driven models are envisaged and deployed to develop several advanced control approaches for HST systems

RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme