scholarly journals Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng Wang ◽  
Tongtong Ji ◽  
Feng Mao ◽  
Zhenpo Wang ◽  
Zhiheng Li
Keyword(s):  
Life Cycle ◽  
Fault Diagnosis ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Research Progress ◽  
Prognostics And Health Management ◽  
Time Data ◽  
Real Time Data

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

scholarly journals Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094432
Author(s):  
Xiaowei Xu ◽  
Xue Qiao ◽  
Nan Zhang ◽  
Jingyi Feng ◽  
Xiaoqing Wang
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Development Trend ◽  
Operating Conditions ◽  
Intelligent Fault Diagnosis ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors

Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

Research on Real-Time Monitoring of Coal Mine Trackless Rubber Tyre Electric Vehicle

2014 ◽  
Vol 595 ◽  
pp. 175-179
Author(s):  
Chuan Wei Zhang ◽  
Ming Yu Wang
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Monitoring System ◽  
Coal Mine ◽  
Data Monitoring ◽  
Real Time Monitoring ◽  
Time Data ◽  
Real Time Data ◽  
Monitoring Information

The monitoring information of the coal mine trackless rubber tyre electric vehicle is analyzed and researched. The hardware and software of real-time data monitoring system electric vehicle is designed based on the TMS320F2812 DSP chip and LCD module MS12864. The coal mine trackless rubber tyre electric vehicle real-time data monitoring is achieved.

Carbon management framework for sustainable manufacturing using life cycle assessment, IoT and carbon sequestration

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shraddha Mishra ◽  
Surya Prakash Singh
Keyword(s):  
Life Cycle ◽  
Carbon Sequestration ◽  
Real Time ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Carbon Sink ◽  
Time Data ◽  
Content Type ◽  
Real Time Data

Purpose Emission reduction methodologies alone are not sufficient to mitigate the climatic catastrophes caused due to ongoing carbon emissions. Rather, a bidirectional approach is required to decarbonize the excess carbon in the atmosphere through carbon sequestration along with carbon reduction. Since the manufacturing sector contributes heavily to the ongoing carbon emissions, the purpose of this paper is to propose a framework for carbon emission reduction and carbon sequestration in the context of the manufacturing industry. Design/methodology/approach In this paper, life cycle assessment (LCA) is employed to track the carbon emission at each stage of the product development life cycle. The pre-requisite for this is the accurate evaluation of the carbon emissions. Therefore, IoT technologies have been employed for collecting real-time data with high credibility to perceive environmental impact caused during the entire life cycle of the product. The total carbon emission calculation is based on the bill of material (BOM)-based LCA of the product to realize the multi-structure (from parts and components to product) as well as multi-stage (from cradle to gate) carbon emission evaluation. Carbon sequestration due to plantation is evaluated using root-shoot ratio and total biomass. Findings A five interwoven layered structure is proposed in the paper to facilitate the real-time data collection and carbon emission evaluation using BOM-based LCA of products. Further, a carbon neutral coefficient (CNC) is proposed to indicate the state of a firm’s carbon sink and carbon emissions. CNC=1 indicates that the firm is carbon neutral. CNC >1 implies that the firm’s carbon sequestration is more than carbon emissions. CNC <1 indicates that the firm’s carbon emission is more than the carbon sink. Originality/value The paper provides a novel framework which integrates the real-time data collection and evaluation of carbon emissions with the carbon sequestration.

Real-Time Data-Based Fault Diagnosis System

2011 ◽  
Vol 189-193 ◽  
pp. 2621-2624
Author(s):  
Hai Wang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Historical Data ◽  
Prototype System ◽  
Time Data ◽  
Diagnosis System ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Diagnosis Method ◽  
Fault Diagnosis System

Mining from the equipment/technology process historical data can find diagnosis knowledge. Real-time analysis and evaluation on the status of equipment can be realized based on its current state parameters and historical information, which detect the potential fault rapidly and protect equipment to avoid failures further. This paper did an in-depth study in real-time data-based fault diagnosis system, built a real-time data integration platform and accomplished intelligent diagnosis method by ANFIS networks with the mined historical data. The production process was diagnosed and evaluated online with this diagnosis method. Combined with actual production system, its prototype system was developed.

scholarly journals Fault Diagnosis Model and Application of Water Injection Well Based on SPC Rules and Real-time Data Fusion

2021 ◽  
Vol 2095 (1) ◽  
pp. 012091
Author(s):  
Chunfeng Zheng ◽  
Ang Li ◽  
Song Wang ◽  
Cheng An ◽  
Chaodong Tan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Real Time ◽  
Water Injection ◽  
Operating Parameters ◽  
Application Research ◽  
Time Data ◽  
Injection Wells ◽  
Real Time Data ◽  
Diagnosis Model

Abstract Based on the Extended SPC rules and real-time data fusion, the operating fault diagnosis model and application research of water injection wells were carried out. The historical operating parameters of water injection wells were studied, a multi-parameter rule fault chart was established, and the weight factors of fault diagnosis parameters were obtained based on expert Pcores. A operating fault diagnosis model of water injection wells based on SPC rules and real-time data fusion was constructed. The example analysis shows that the model has good adaptability in Bohai oilfield, can diagnose the fault of water injection wells in real time, quantitatively and accurately, and improves the intelligent level of real-time fault diagnosis of water injection wells.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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