A Framework for the Capture and Analysis of Product Usage Data for Continuous Product Improvement

计算机科学 背景(考古学) 产品生命周期 产品设计说明书 产品(数学) 新产品开发 产品设计 产品工程 系统工程 工程类 几何学 数学 生物 业务 古生物学 营销
作者
Henning Voet,Max Altenhof,Max Ellerich,Robert Schmitt,Barbara Linke
出处
期刊:Journal of Manufacturing Science and Engineering-transactions of The Asme [ASME International]
卷期号:141 (2) 被引量:24
标识
DOI:10.1115/1.4041948
摘要

Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lizili发布了新的文献求助20
刚刚
3秒前
巴山夜雨发布了新的文献求助10
4秒前
Henry应助diu采纳,获得500
6秒前
sybil完成签到,获得积分20
9秒前
9秒前
劲秉应助真真采纳,获得30
9秒前
Orange应助Aurora采纳,获得10
9秒前
友好白凡发布了新的文献求助10
14秒前
mufcyang完成签到,获得积分10
16秒前
西伯利亚彪悍前妻完成签到,获得积分10
17秒前
shangxinyu完成签到,获得积分10
20秒前
maox1aoxin应助认真搞学习采纳,获得30
20秒前
20秒前
博闻完成签到,获得积分20
21秒前
22秒前
23秒前
xmmm发布了新的文献求助10
23秒前
23秒前
糖糖发布了新的文献求助10
24秒前
艾查恩完成签到,获得积分10
26秒前
26秒前
27秒前
Aurora发布了新的文献求助10
28秒前
28秒前
Josh应助wangwnagm采纳,获得20
29秒前
博闻发布了新的文献求助10
29秒前
30秒前
张涛发布了新的文献求助10
31秒前
32秒前
奋斗靖仇完成签到 ,获得积分10
34秒前
伴奏小胖发布了新的文献求助10
36秒前
友好白凡完成签到,获得积分10
36秒前
37秒前
38秒前
小半圆满完成签到,获得积分10
39秒前
糊涂的电话给糊涂的电话的求助进行了留言
40秒前
40秒前
香蕉觅云应助张涛采纳,获得10
40秒前
Akim应助西伯利亚彪悍前妻采纳,获得10
40秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
ALA生合成不全マウスでの糖代謝異常の分子機構解析 520
安全防范技术与工程 500
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
A real-time energy management strategy based on fuzzy control and ECMS for PHEVs 400
2024 Medicinal Chemistry Reviews 400
Why I Chose China [by Morris R. Wills] in "Look", February 8 and 22, 1966; 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3191464
求助须知:如何正确求助?哪些是违规求助? 2840803
关于积分的说明 8030067
捐赠科研通 2504173
什么是DOI,文献DOI怎么找? 1337467
科研通“疑难数据库(出版商)”最低求助积分说明 638097
邀请新用户注册赠送积分活动 606605