Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

This study deals with two types of thermal deformation of ultraprecision machine tool equipped with oil-lubricated hydrostatic bearings; one is caused by temperature fluctuation in the supply lubricating oil and the other which have not yet been investigated is caused by hydrostatic pressure fluctuation. First, it was experimentally found that such thermal deformation. Second, as a method to minimize such thermal deformation, the highprecision regulator was proposed, which could precisely control the temperature and pressure of the supply lubricating oil, and experimentally evaluated the effectiveness of this method. As the experimental result, this method showed that temperature fluctuation of the ultraprecision machine tool can be restricted to the order of a ten-thousandth of a degree C, and that thermal deformation can be restricted to within several nanometers.

Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning

A23 - Selection of heaters and coolers

At the University of Nevada, Reno, we strongly believe that effective control system education must include experimental exercises that complement the theory presented in lectures. Preferably, the exercises should include the design and implementation of a control system. Limited resources and a cap on the number of credit hours required for the undergraduate degree make it impossible to offer a laboratory course with every control class in most electrical engineering curricula. We have solved this problem by including a laboratory project in each of our two senior electives. Each project is a comprehensive modeling, analysis, design and implementation of a physical control system. The students are divided into groups and each group must independently complete the design project and submit a formal report summarizing their results and experiences. Each student must submit an individual commentary on the exercise and the experimental results obtained. Student feedback indicates an increased appreciation of the lecture material and an awareness of the limitations of the theory and simulation that was lacking prior to the introduction of laboratory exercises.

A high speed half-toroidal continuously variable power transmission, CVT, has been developed. The transmission consists of the CVT with a ratio range from 2.0 to 0.5 and of a final reduction gear-set of 4.78. Some design features of the CVT are minimum spin on the traction contact, application of ceramic materials to thrust ball bearings and loading-cam rollers, and 21 MPa electro hydraulic speed control servomechanism for keeping the output-shaft rotational speed at 3000 rpm against input rotational speed changes from 10000 to 20500 rpm and load torque from 0 to 175 Nm. The CVT works at a speed transmission efficiency of 98% and a torque transmission efficiency of 85% in whole input speed ranges. This paper describes that the experimental micro-slip can be predicted by the theoretical spin analysis.

Integrative modeling and simulation analysis of 11kW squirrel-cage asynchronous motor and vector control system have been done in the paper. Integrative model deals with the coupling effect between the motor and its control system, and analyzes system dynamic response. At the same time, the motor transient electromagnetic field performance has been acquired in the integrative simulation with slip frequency vector control strategy. Furthermore, the simulation results of integrative model are compared with that of non-integrative model, including control system circuit model and motor physical model without precise control strategy. The integrative model considers not only the motor external control strategy but also motor internal electromagnetic field performance and response with the control strategy. In addition, integrative modeling and simulation realizes coupling simulation analysis of squirrel-cage asynchronous motor and control system in system perspective and component perspective as well, which could effectively help us do the precise analysis of system performance.

Extended Dynamic Mode Decomposition with Invertible Dictionary Learning

COMPUTER AIDED INSTRUCTION SYSTEM WITH AN INVERTED PENDULUM EQUIPMENT

To accurately determine the transmission accuracy degradation pattern of Rotate Vector (RV) reducers due to wear, a predictive method is proposed that simultaneously considers the initial clearance and wear clearance between the cycloid wheel and the pinwheel. The initial clearance is derived from a comprehensive gear system analysis, considering assembly errors, manufacturing errors and modifications. Building on this, the wear depth of the cycloid wheel tooth profile is numerically simulated using Archard's wear theory, revealing the wear distribution pattern under various operating conditions. A Gaussian regression model is used to predict the maximum wear amount for different operational durations. Based on the predicted wear amount, a multi-body dynamic simulation model incorporating wear clearance was established to analyze the transmission error under varying clearance conditions. Results indicate that the wear depth along the tooth profile initially increases and then decreases. The depth and region of wear along the tooth profile gradually increase with load and output speed, with the impact of load on the wear region being greater than that of output speed. Under the initial clearance condition, the maximum transmission error is 22.91″. As the wear clearance increases, transmission accuracy declines sharply. When the cumulative wear clearance reaches 0.2 mm, the transmission error exceeds the allowable backlash limit. This study provides a theoretical reference for predicting accuracy degradation and guides the manufacturing design of RV reducers.

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a probabilistic generative model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of generative models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.

This paper presents a novel method of neural network and observer compensation control for 6-dof virtual-axis machine tool driven by permanent magnetic linear synchronous motor (PMLSM) by keeping the 6 links in cooperating motion. The coupling 6-link system, in this paper, is divided into 6 separate systems, and each link's load disturbance and the coupling disturbance are regarded as the dynamic parameters' variation of the PMLSM. An observer is presented to identify the parameters' variation and to compensate for propulsive current i <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> of PMLSM through adjusting the weights of neural network IP position controller, which eliminates the coupling and obviates a lot of cumbersome dynamic calculations of the parallel mechanism. In each separated system, the position controller is adjusted on-line depending on the position error and the speed command to be appropriate for the time variation of the position and speed, and the speed controller is realized by one neuron, which can be rapidly trained on-line, the acceleration control is attained indirectly by the proposed observer and neuron, which improves tracking performance of the position and speed output. The results of simulation indicate that the presented control strategy used in the position, speed and acceleration of each link can be realized, and the system has a good robustness. Conclusions can be drawn that the new method can satisfy the control performance require of 6-dof virtual-axis machine tool, at the same time, this control strategy is reasonable and feasible.

This paper proposed a wireless, multi-parameter plant growth monitoring and control system for quality agriculture application. With the collection and analytics of one-week weather forecast information, the developed system can optimize the plant growth condition by adjusting major growth parameters including environmental temperature, humidity, soil water and illumination data and controlled actuators for proper irrigation rate, fan operation and ultra-sound water level monitoring. Meanwhile, the real-time observation and system performance by an experimental lettuce growth are verified through the selected-ratio LEDs arrangement.

PurposeThe purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance.Design/methodology/approachThe proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system.FindingsThe results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives.Research limitations/implicationsThis research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level.Originality/valueThis present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions.

This paper addresses the speed control of a series DC motor. Based on the nonlinear motor system dynamics, the analysis and design of two novel speed controllers are presented, which are nonlinear PI and integrator backstepping controllers. The speed response following step changes in speed reference and load torque, using the proposed nonlinear controllers and traditional linear PI controllers, is compared to evaluate their merits. It is shown that the proposed controllers can provide stable and more excellent dynamic control performance than that of the traditional PI controllers in terms of rise time, overshoot and steady state error.