Production safety training using networked digital intelligent systems

2021 ◽  
pp. 10-20
Author(s):  
E.V. Khalin
Keyword(s):  
Intelligent Systems ◽  
Graphical Representation ◽  
Distributed Databases ◽  
Simulation Models ◽  
Industrial Safety ◽  
Training Systems ◽  
Safe Production ◽  
Production Safety ◽  
Digital Production ◽  
And Control

The preparation and control of the knowledge of those working on industrial safety using networked digital intelligent learning systems containing formalized knowledge in text and graphical representation and simulation models in the form of distributed databases and knowledge is considered as the most important element of the organization of safe production based on digital technologies. When integrating training systems into a specific digital production, educational content, if necessary, is supplemented and developed by those responsible for training and the students themselves as part of the educational resource of digital organizations for training and retraining personnel.

Training of workers in production safety using digital technologies

2021 ◽  
pp. 179-186
Author(s):  
E.V. Halin
Keyword(s):  
Graphical Representation ◽  
Personal Information ◽  
Distributed Databases ◽  
Essential Element ◽  
Digital Technologies ◽  
Simulation Models ◽  
Industrial Safety ◽  
Digital Learning ◽  
Production Safety ◽  
And Control

The preparation and control of the knowledge of workers in the field of industrial safety using networked digital intelligent learning systems containing formalized knowledge in text and graphical representation and simulation models in the form of distributed databases and knowledge is considered as an essential element of the organization of safe production based on digital technologies. When integrating training systems into a specific digital production, educational content, if necessary, is supplemented and developed by those responsible for training and the learners themselves as part of the educational resource of digital organizations for training and retraining personnel. Keywords digital learning; intelligent technologies; production safety; digital manufacturing; personal information; information security; digital instruction; digital certification

Functions of responsible for training in digital systems for industrial safety

2021 ◽  
pp. 28-33
Author(s):  
E.V. Khalin
Keyword(s):  
Safety Training ◽  
Training System ◽  
Digital Systems ◽  
Industrial Safety ◽  
Software Complex ◽  
Emergency Situations ◽  
Functional Capabilities ◽  
Production Safety ◽  
Digital Production ◽  
Effective Training

The functional capabilities provided by the digital production safety training system for those responsible for training allow the software complex to be maintained in a stable operational state when exposed to emergency situations, to fulfill all the necessary needs of responsible users with the authority to create effective training programs and to test the knowledge of workers on production safety, to quickly form up-to-date digital reporting documentation for the organization.

Study on Production Safety Accident Prevention and Control Method

2012 ◽  
Vol 452-453 ◽  
pp. 1035-1038
Author(s):  
Rui Hua Lv ◽  
Ying Ying Kang
Keyword(s):  
Prevention And Control ◽  
Control Method ◽  
Hebei Province ◽  
Monitoring Method ◽  
Safe Production ◽  
Production Safety ◽  
Production Run ◽  
Safety Accident ◽  
And Control ◽  
Production Conditions

In this paper, there is an analysis of the situation of production safety accidents that is the "Eleventh Five-Year" period, Hebei Province in China. The characteristics of production safety accidents and laws will be revealed. The monitoring method of safe production run will be proposed as the basis of decision making for the safe production conditions can be effectively controlled.

Design and Simulation of Pedal Simulator in Brake by Wire System

2014 ◽  
Vol 556-562 ◽  
pp. 1358-1361 ◽  
Author(s):  
Wen Bo Zhu ◽  
Fen Zhu Ji ◽  
Xiao Xu Zhou
Keyword(s):  
Simulation Models ◽  
Control Algorithms ◽  
Brake Pedal ◽  
Braking System ◽  
Pedal Force ◽  
Performance Requirements ◽  
System A ◽  
Pedal Feel ◽  
And Control ◽  
Wire System

Wire of the brake pedal is not directly connected to the hydraulic environment in the braking By-wire system so the driver has no direct pedal feel. Then pedal simulator is an important part in the brake-by-wire system. A pedal force simulator was designed based on the traditional brake pedal curve of pedal force and pedal travel, AMESim and Matlab / Simulink were used as a platform to build simulation models and control algorithms. The simulation results show that the pedal stroke simulator and the control strategy meet the performance requirements of traditional braking system. It can be used in brake by wire system.

Rotorcraft Lateral-Directional Oscillations: The Anatomy of a Nuisance Mode

2021 ◽  
Author(s):  
Dheeraj Agarwal ◽  
Linghai Lu ◽  
Gareth D. Padfield ◽  
Mark D. White ◽  
Neil Cameron
Keyword(s):  
Simulation Model ◽  
Simulation Models ◽  
Flight Test ◽  
Flight Simulation ◽  
Stability And Control ◽  
Systems Research ◽  
Control Derivatives ◽  
Research Aircraft ◽  
Level Of Understanding ◽  
And Control

High-fidelity rotorcraft flight simulation relies on the availability of a quality flight model that further demands a good level of understanding of the complexities arising from aerodynamic couplings and interference effects. One such example is the difficulty in the prediction of the characteristics of the rotorcraft lateral-directional oscillation (LDO) mode in simulation. Achieving an acceptable level of the damping of this mode is a design challenge requiring simulation models with sufficient fidelity that reveal sources of destabilizing effects. This paper is focused on using System Identification to highlight such fidelity issues using Liverpool's FLIGHTLAB Bell 412 simulation model and in-flight LDO measurements from the bare airframe National Research Council's (Canada) Advanced Systems Research Aircraft. The simulation model was renovated to improve the fidelity of the model. The results show a close match between the identified models and flight test for the LDO mode frequency and damping. Comparison of identified stability and control derivatives with those predicted by the simulation model highlight areas of good and poor fidelity.

Digitale Produktion und intelligente Steuerung/Digital Production and Intelligent Production Control – Integrating digital and real-world production for adaptive and automated control

2020 ◽  
Vol 110 (04) ◽  
pp. 255-260
Author(s):  
Marvin Carl May ◽  
Andreas Kuhnle ◽  
Gisela Lanza
Keyword(s):  
Production Control ◽  
Production Systems ◽  
Industrie 4.0 ◽  
World Production ◽  
Production Planning And Control ◽  
Automated Control ◽  
Planning And Control ◽  
Digital Production ◽  
Digitale Modelle ◽  
And Control

Im Rahmen der stufenweisen Umsetzung von Industrie 4.0 erreicht die Vernetzung und Digitalisierung die gesamte Produktion. Den physischen Produktionsprozess nicht nur cyber-physisch zu begleiten, sondern durch eine virtuelle, digitale Kopie zu erfassen und zu optimieren, stellt ein enormes Potenzial für die Produktionssystemplanung und -steuerung dar. Zudem erlauben digitale Modelle die Anwendung intelligenter Produktionssteuerungsverfahren und leisten damit einen Beitrag zur Verbreitung optimierender adaptiver Systeme.   In the wake of implementing Industrie 4.0 both integration and digitalization affect the entire production. Physical production systems offer enormous potential for production planning and control through virtual, digital copies and their optimization, well beyond purely cyber-physical production system extensions. Furthermore, digital models enable the application of intelligent production control and hence contribute to the dissemination of adaptively optimizing systems.

SMoBAICS

2017 ◽  
Vol 5 (2) ◽  
pp. 34-57
Author(s):  
Volkhard Klinger
Keyword(s):  
Data Acquisition ◽  
Simulation Models ◽  
Identification System ◽  
Animal Testing ◽  
Cuff Electrode ◽  
Platform Architecture ◽  
Computer Aided ◽  
Verification Systems ◽  
Application Fields ◽  
And Control

Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.

Model-Based Simulation to Examine Command and Control Issues with Remotely Operated Vehicles

2008 ◽  
pp. 199-218 ◽  
Author(s):  
Sasanka Prabhala ◽  
Subhashini Ganapathy ◽  
S. Narayanan ◽  
Jennie J. Gallimore ◽  
Raymond R. Hill
Keyword(s):  
Cognitive Model ◽  
Command And Control ◽  
Simulation Models ◽  
High Fidelity ◽  
Remotely Operated Vehicles ◽  
Design Concepts ◽  
Model Based ◽  
Critical Issues ◽  
Commercial Applications ◽  
And Control

With increased interest in the overall employment of pilotless vehicles functioning in the ground, air, and marine domains for both defense and commercial applications, the need for high-fidelity simulation models for testing and validating the operational concepts associated with these systems is very high. This chapter presents a model-based approach that we adopted for investigating the critical issues in the command and control of remotely operated vehicles (ROVs) through an interactive model-based architecture. The domain of ROVs is highly dynamic and complex in nature. Hence, a proper understanding of the simulation tools, underlying system algorithms, and user needs is critical to realize advanced simulation system concepts. Our resulting simulation architecture integrates proven design concepts such as the model-view-controller paradigm, distributed computing, Web-based simulations, cognitive model-based high-fidelity interfaces and object-based modeling methods.

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