Bosch air compressors for cellcentric trucks

2021 ◽  
Vol 2021 (7) ◽  
pp. 13-14
Keyword(s):  
Air Compressors

scholarly journals Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors

2021 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed
Keyword(s):  
Transfer Learning ◽  
Data Transfer ◽  
Health Management ◽  
Correct Diagnosis ◽  
Vital Role ◽  
Research Field ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Classification Problems ◽  
Air Compressors

Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

Thermoeconomic Model for the Energy Optimization of Industrial Air Compressed Supply Network Under Transient Condition

2012 ◽  
Author(s):  
Lesme Corredor M. ◽  
Diego Guillen ◽  
José Prada ◽  
Alisson Contreras
Keyword(s):  
Energy Demand ◽  
Energy Optimization ◽  
Industrial Plant ◽  
Transient Condition ◽  
Power Demand ◽  
Flow Monitoring ◽  
Air Supply ◽  
Air Compressors ◽  
Air Compression ◽  
Selection Of

Air compression represents around 20% of industrial total electric power demand, especially in chemicals and process companies. Few technical studies related with energy optimization of air compressed networks are reported in the specialized literature, in contrast, in natural gas and steam networks have been widely analyzed. Pressure, temperature and flow monitoring of air compression is not enough for implementation of energy optimization models, for this reason authors have developed a transit conditions model which takes into account air supply equipments and air compressed process requirements. This paper presents a decision support system for the scheduling selection of a set of air compressors in an industrial plant based on energy demand minimization. Several constraints must be taken in consideration during the optimization process, this can be desegregate in two types, the first set of constrains was used for simulate the operation of scroll, screw and centrifuges compressors, the second based in graph an node theory and contain the mathematical transit conditions model of supply air network topology, for the complexity of the problem the use of a genetic algorithm to search an optimal combination was necessary.

scholarly journals The pneuPAC ventilator with new patient valve and air compressors

Anaesthesia ◽  
1989 ◽  
Vol 44 (5) ◽  
pp. 419-424 ◽  
Author(s):  
G. R. PARK ◽  
A. R. MANARA ◽  
A.R. BODENHAM ◽  
C. J. MOSS
Keyword(s):  
Air Compressors

Smart Factory Meets Smart Grid: Cyber-Physical Compressed Air Systems Enable Demand Side Management in Industrial Environments

2015 ◽  
Vol 805 ◽  
pp. 25-31 ◽  
Author(s):  
Ralf Boehm ◽  
Johannes Bürner ◽  
Jörg Franke
Keyword(s):  
Business Models ◽  
Service Providers ◽  
Supply And Demand ◽  
Electric Energy ◽  
Demand Side Management ◽  
Compressed Air ◽  
Demand Side ◽  
Industrial Enterprises ◽  
Industrial Environments ◽  
Air Compressors

In electric energy systems based on renewable generation plants supply and demand often do not occur in the same period of time. Consequently demand side management is gaining importance whereby decentralized automation offers opportunities in industrial environments. Compressed air systems on industrial plants consist of air compressors, compressed air reservoirs and compressed air lines. With suitable dimensioning those industrial compressed-air systems can be used for demand side management purpose. As power consumption of industrial air compressors ranges between a few and several hundred kilowatts each, swarms of communicatively connected air compressors can contribute to the stabilization of power grids. To avoid costly production downtime it is to ensure, that a reliable, non-disruptive supply of compressed air can be maintained at all time. Industrial compressed air systems equipped with automation technology and artificial intelligence, which hereinafter are referred to as Cyber-Physical Compressed Air Systems (CPCAS), allow new business models for utilities, industrial enterprises, compressor manufacturers and service providers. In addition to basic operating parameters like current air pressure and status, those systems can process further information and create, for example, profiles on compressed air consumption over time. By enriching those profiles with data on pressure, volumes, system restrictions and current production requirements (plans), the CPCAS can identify the available potential for demand side management. Ipso facto predictive power on electricity consumption is increasing. By providing the information obtained to the power company or a service provider, savings in electricity costs may be achieved. Expenses within the industrial company may be lowered further as compliance with agreed load limits is being improved by automatic shutdown of air compressors upon reaching the load limit. Within this article the structure of the aforementioned Cyber-Physical Compressed Air Systems is presented in more detail, relations between the major actors are being shown and possible business models are being introduced.

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