This article presents an approach for using Multivariate Time Series Classification (MTSC) to determine the basic production phases from the data of a numerical control (NC) of a manufacturing machine. The milling pro...
详细信息
ISBN:
(纸本)9798331505981;9798331505974
This article presents an approach for using Multivariate Time Series Classification (MTSC) to determine the basic production phases from the data of a numerical control (NC) of a manufacturing machine. The milling process in a series production of a contract manufacturer in Germany was selected as the use case. The MTSC techniques LSTM, 1D-CNN, and LSTM-FCN were selected for modeling the classification approach. All modeling phases are explained and finally, these techniques are compared with a standard neural network approach that performs classification time-independent. Overall, using 1D-CNN networks showed a clear superiority over the other models.
The Spin-Exchange Relaxation-Free Comagnetometer (SERFCM) is a new quantum instrument with ultra-high accuracy. Normally, the atomic ensembles of SERFCM operate in an open-loop state, which is not conducive to long-te...
详细信息
ISBN:
(纸本)9798350321050
The Spin-Exchange Relaxation-Free Comagnetometer (SERFCM) is a new quantum instrument with ultra-high accuracy. Normally, the atomic ensembles of SERFCM operate in an open-loop state, which is not conducive to long-term highprecision measurements. In order to realize closed-loop control of its atomic polarization state, it is necessary to model and analyze the dynamic characteristics of the SERFCM system. In this paper, a data-driven physical mechanism (DDPM) modeling method is proposed to realize the modeling of the SERFCM, a multi-input multi-output system. First, the state space equations of the SERFCM are established based on the Bloch equation, which are transformed into a discrete transfer function matrix. Then, based on the criterion of least variance in estimation, we realize the modeling of the discrete transfer function matrix using the excitation input data, the measured output data, and the estimated output data. Finally, the simulation results of modeling under different longitudinal magnetic fields confirm the validity of the proposed method. This work enables the online modeling of SERFCM system and facilitates the analysis of the effects of various parameters on the dynamic characteristics.
Inverse modeling is the process uncovering the relationships from the system observations to its inputs. It is essential in various fields such as control, robotics, and signal processing. We propose an inverse modeli...
详细信息
Heavy-duty vehicle security is critical to safeguarding the supply chain of essential goods and materials. Implementing a continuous driver authentication system based on unique driving behaviors enhances security thr...
详细信息
ISBN:
(纸本)9798350365924;9798350365917
Heavy-duty vehicle security is critical to safeguarding the supply chain of essential goods and materials. Implementing a continuous driver authentication system based on unique driving behaviors enhances security through improved access control without the use of personally identifiable information (PII). This ensures that only authorized drivers operate the vehicle and reduces the risk of unauthorized use. Designing this complex system to reduce this risk can be done effectively through SysML modeling and MagicGrid systems modeling. Through the use of systems modeling, this study was split into two main objectives. The first objective is to develop this driver authentication system by analyzing behavior patterns through three sets of vehicle sensor data: CANbus, GPS, and IMU data. These collect extensive vehicle data like vehicle speed, engine speed, pedal positions, location, and heading degree. This data is then used to train several different machine learning models to create a driver profile report for identity verification. The second objective is to run cyberattacks on the vehicle and analyze the driver's behavior with physiological data like heart rate variability and with the vehicle sensor data. The cyberattacks cause a malfunction on the vehicle's instrument cluster by sending various messages to the CANbus. This is used to create a report on how drivers respond in high pressure conditions, which can be used to train drivers transporting hazardous material to react properly in these situations. The purpose of this paper is to show how the application of the MagicGrid method and SysML modeling improved the development of the stakeholder needs, requirements, use cases, and system structure.
With the increasing complexity of aircraft, document-based systems engineering has gradually become inadequate, and model-based systems engineering has become a hot issue in the aviation industry and academia. In view...
详细信息
In recent years, the safety of complex system applications has received increasing attention. The SLIM language provides a cohesive and unified approach to modeling systems in the aerospace domain. However, there are ...
详细信息
With the advent of the big data era and the timeliness requirements of dataprocessing, a large amount of streaming industrial big data is continuously obtained in real time. Facing this kind of flowing and time-varyi...
详细信息
With the advent of the big data era and the timeliness requirements of dataprocessing, a large amount of streaming industrial big data is continuously obtained in real time. Facing this kind of flowing and time-varying knowledge information form, incremental learning is necessary. Lifelong learning (LL), as a typical incremental learning method, can continuously retain and accumulate old knowledge while learning new knowledge, which is very suitable for streaming industrial big data scenarios. At the same time, Bayesian nonparametric (BNP) models can adjust the complexity of the model based on observation data. Motivated by ideas of BNP and LL, a lifelong Bayesian learning machines framework is proposed in this article, which includes model expansion and model optimization. In general, this framework not only learns new effective knowledge and accumulates knowledge through incremental variational Bayesian under model expansion but also uses optimization steps to avoid model degradation caused by unnecessary component information. As an example, Dirichlet processes Gaussian mixture regression (DPGMR) is utilized for processmodeling under this framework. To evaluate the feasibility and efficiency of the developed method, a synthetic and a real industrial case are demonstrated.
Multi-party collaborative modeling allows different participants to build machine learning models without revealing the local data. Federated Learning (FL) is currently the main technique to achieve multi-party collab...
详细信息
With over 80% of Malaysia's fields relying on gas lift, a late life crisis is evident, prompting secondary and tertiary production enhancements to sustain oil production. Challenges like gas lift gas shortages, ag...
详细信息
ISBN:
(纸本)9781959025436
With over 80% of Malaysia's fields relying on gas lift, a late life crisis is evident, prompting secondary and tertiary production enhancements to sustain oil production. Challenges like gas lift gas shortages, aging facilities, and increased water cut possess efficient oil recovery limitations, which fueled the demand for an alternative lift technology in offshore Malaysia brown fields. However, replacing a gas lift system is a tedious task. This study proposes an automated screening method swiftly identify candidates, reducing the time and workforce needed to select from hundreds of wells and streamlining production optimization activities for brown fields selected in offshore Malaysia. This data input and analysis automation system will be tested with data from multiple fields to pre-screen the strings and establish a basket of opportunities before proceeding with screening using well modeling. From the vast number of attributes used in this study, essential parameters that will be used for candidate screening have been identified. The automation of this data for screening enables narrowing down the number of potential candidates for modeling and detailed analysis. Electrical submersible pump (ESP) design and economic analysis will be done once the candidate has been finalized through well modelling. Critical parameters such as remaining reserve, absolute open flow, well angle, dog leg severity, the latest liquid rate, the latest oil rate, the latest water cut, the latest gas oil ratio, sand count and identified integrity issues are essential for a production enhancement activity. By digitalizing this data, automation will ease the path to identify a pool of production enhancement candidates out of hundreds of strings in a field. The identified candidate will proceed to the maturation process where well modeling, design, and economic analysis will be conducted. This process saves time and increases the number of candidates available for production enhancement act
This brief studies the multi-rate optimal control problem for a class of industrial processes, whose controlling rate will be set faster than the sampling rate sometimes. This multi-rate phenomenon makes the accurate ...
详细信息
This brief studies the multi-rate optimal control problem for a class of industrial processes, whose controlling rate will be set faster than the sampling rate sometimes. This multi-rate phenomenon makes the accurate modeling of control systems challenging and difficult. In this brief, we present a model-free self-learning control scheme for the real-time solution of this problem, combining the lifting technology and Q-learning. For the asynchronous periods, the lifting system is established first to reconstruct the input and output by stacking the control and sampling signals to a frame period, maintaining the original dynamic information. Then, Q-learning is adopted to learn the optimal control policy with the real-time data and the convergence analysis of the proposed algorithm is derived. In this way, the control actions are executed at a faster rate to obtain the better dynamic performance. Finally, a hardware-in-loop (HIL) simulation study for process industries is carried out, showing that the proposed approach has high tracking and real-time performance.
暂无评论