Senseye
概述
总部
英国
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成立年份
2014
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公司类型
私营公司
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收入
< $10m
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员工人数
51 - 200
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网站
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推特句柄
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公司介绍
Senseye 是领先的基于云的预测性维护软件。它通过自动预测机器故障而无需专家手动分析,帮助制造商避免停机并节省资金。其智能机器学习算法使其可用于任何制造商的任何机器,从现有的工业物联网传感器和平台获取信息,以自动诊断故障并提供机器的剩余使用寿命。
物联网解决方案
物联网应用简介
Senseye 是应用基础设施与中间件, 分析与建模, 功能应用, 传感器, 和 基础设施即服务 (iaas)等工业物联网科技方面的供应商。同时致力于汽车, 建筑与基础设施, 电子产品, 石油和天然气, 和 零售等行业。
技术栈
Senseye的技术栈描绘了Senseye在应用基础设施与中间件, 分析与建模, 功能应用, 传感器, 和 基础设施即服务 (iaas)等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
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实例探究.
Case Study
Predictive maintenance in Schneider Electric
Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.
Case Study
Scalable Predictive Maintenance in Nissan
With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.
Case Study
Nissan Manufactures Vehicles in 20 Countries
With an abundance of sensor data but insufficient skilled resources to perform manual analysis, Nissan was keen to expand the benefits of using data and machine learning to influence maintenance. In 2016, it decided to embark on a Predictive Maintenance program to reduce production downtime by up to 50% across thousands of diverse machines.It was attracted to Senseye by its deep domain experience and ability to scale across its sites, underpinned by its patented Artificial Intelligence technology.
Case Study
Scalable Predictive Maintenance in INSEE
SCCC had committed to running a showcase Digital Factory for the ASEAN region and had already invested heavily in smart factory equipment and sensors. They required a predictive maintenance system that would leverage their existing investments and integrate with their SAP PM maintenance system.
Case Study
Canadian Energy Firm Started Its Digital Transformation
Seeks to improve operational decision-making, safety management and sustainability.A key element in these initiatives is asset maintenance, representing approximately 25% of Cameco’s overall operating costs at its mining operations. Improving asset management began with automating data collection.Cameco had struggled to analyze multiple regular condition monitoring data from assets in the past. As a result, the company found it hard to understand what went wrong in the event of asset failure.
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