Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Chaotic information-geometric support vector machine and its application to fault diagnosis of hydraulic pumps

Fault diagnosis of rotating machineries is becoming important because of the complexity of modern industrial systems and the increasing demands for quality, cost efficiency, reliability, and safety. In this study, an information-geometric support vector machine used in conjunction with chaos theory (chaotic IG-SVM) is presented and applied to practical fault diagnosis of hydraulic pumps, which are critical components of aircraft. First, the phase-space reconstruction of chaos theory is used to determine the dimensions of input vectors for IG-SVM, which uses information geometry to modify SVM and improves performance in a data-dependent manner without prior knowledge or manual intervention. Chaotic IG-SVM is trained by using the dataset from the normal state without fault, and a residual error generator is then designed to detect failures based on the trained chaotic IG-SVM. Failures can be diagnosed by analyzing residual error. Chaotic IG-SVM can then be used for fault clustering by analyzing residual error. Finally, two case studies are presented, and the performance and effectiveness of the proposed method are validated

Identification of Water Hammering for Centrifugal Pump Drive Systems

Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size

Predictive model for the degradation state of a hydraulic system with dimensionality reduction

In recent years, the optimization in the use of resources has a key role in achieving a bigger marginality, reducing the operative costs. Due to the advances in the data science field, even the maintenance context is living important changes. The predictive maintenance and the condition-based maintenance can overcome the classic traditional maintenance methods, like the time-based maintenance or the corrective maintenance, with respect to the first intervention, reducing the costs for unscheduled maintenance, manpower, or loss of production and extending the useful life of the components. Based on these presuppositions, the paper proposes the development of a predictive model for the degradation state of the components of a complex hydraulic system, with some tests and some suggestions about the dimensionality reduction. The system has four known types of breakdown, with different degrees of severity; moreover, a fifth parameter represents whether the cycle has reached stable conditions or not

Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra

The design and optimization of a condition monitoring device using data reduction techniques to estimate the leakage of a load sensing axial piston pump

Hydraulic systems are commonly used as solutions to industry challenges. Their excellent power-to-weight ratio can achieve specific design criteria that other power methods may not. In many hydraulic components, precision machining is present. This is to provide hydrodynamic lubrication between contacting components. By design, component life is greatly increased due to limited physical part interaction. Subsequently, any changes to the machined surfaces can result in accelerated and even catastrophic damage. Pressure compensated load sensing (PCLS) axial piston pumps are common in heavy duty hydraulic applications and provide flow in hydraulic systems. Typically, when a pump is exposed to common environmental contamination, internal machined surfaces can become damaged in the form of scoring. Depending on the degree of damage, this can result in increased leakage across lubricating boundaries or catastrophic failure due to adhesion. Component failure can then manifest in several ways. On a pump, slight wear can result in increased case drain leakage and the operator may not notice any performance issues, however, catastrophic failure may result in immediate system changes. A current method of evaluating the condition of an axial piston pump is by measuring the case drain leakage flow. This procedure involves installing a test flowmeter between the case drain leakage line and the reservoir and recording the flow at certain pressures. This can be an involved procedure and any time a closed hydraulic circuit is disassembled, the risk of introducing contamination is high. Additionally, robust, heavily used flowmeters can be inaccurate and unreliable due to wear and calibration errors. There is an obvious need to further develop the method of evaluating the health of a load sensing axial piston pump. The research contained in this thesis provides a potential cost effective alternative to case drain flow monitoring of PCLS axial piston pumps through the analysis of dynamic pump data. A nonlinear dynamic model of a load sensing axial piston pump and circuit is developed and validated with experimental dynamic pressure and swash angle position signals. The dynamic response of the pump outlet pressure, control piston pressure, and swashplate angle of a load sensing pump is shown to change with case drain leakage, both with the model and experimentally. iii A statistical procedure, Principal Component Analysis, (PCA), is applied to a large training dataset developed by the dynamic model. PCA is a fundamental piece of the leakage prediction algorithm developed in this research. In a simulation study, the designed leakage prediction algorithm is able to predict leakage using clean training and test data with a root mean square (RMS) error of less than 1%. Further algorithm development includes determining the best dynamic measurements to obtain, the amount of training data, a filter design for the raw experimental data, and training data manipulation. A simulation study shows that the signal combination that gives the best prediction performance is a combination of the pump pressure, control piston pressure, and the swashplate angle. This was confirmed by evaluating the leakage prediction performance with experimental pump response data. Having determined the optimal sensor data, the amount of training data is investigated. This was shown to improve from 100 samples and peak at 1000 samples. An optimization using experimental data was performed to determine the best filter to apply to the experimental response data. It was determined that a low pass filter with a cutoff frequency 10% below the piston pumping frequency gave the best leakage prediction results. This research includes a thorough investigation into the manipulation of the training data. The detailed optimal noise addition parameters give a predictive error of less than 20% using a signal combination of pump pressure, control piston pressure, and swashplate angle for experimental pump response data. Using just the pump and control piston pressure transients results in approximately 40% prediction error. Swashplate response data give conflicting results as the predictive error for the minimally worn pump is much different than the high wear pump (20% for severely worn). This research is an investigation into the feasibility of a load sensing axial piston pump condition monitoring device that measures case drain leakage via dynamic measurements. A comprehensive analysis is performed to optimize a leakage predictive algorithm and the design is tested in simulation as well as with experimental data and shows good potential

Sensitivity analysis of sensors in a hydraulic condition monitoring system using CNN models

Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensorsPeer ReviewedPostprint (published version

Volume 1 – Symposium: Tuesday, March 8

Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Components:Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Component