10孙学海一种通用的工业设备大数据分析方法和实例

1、一种通用的工业大数据分析 以数为据的和实例论 工控界的转型：看和想，与挖掘 我只要数，不要据。解除安全担忧 换个角度去看：海量数据的特征分析 实例：全维度的特征图，发现 实战：传感器部署和移动式 共赢的合作模式车: 未来最多的数据来自于传感器和(CPS)白宫组顾问教授：工业4.0中的各类技术，如传感器技术、云计算等都能找到相应的供应商，但仅仅这些技术还是远远不够的，因为其中最的两个部分，智能化的数据分析服务和模式的创新是的内功，而这恰恰是企业最欠缺去避免和解需要的。工业大数据分析能够帮助决以往不可见的问题，进一步减少成本和提升质量。也可以帮助我们去发掘用户不创值的角度提升可见的需求，从的竞争力

2、。大型装备类客户的感触：大数据作为新一代渐成为新一轮产业析是联机数据分析的业、激发生产力的的关键，逐。大数据分的，是重构全球工。无论是工业4.0、工业互联网还是2025，装备是基础，具备全维度大数据智能智慧管理功能的装备将成为新的发展趋势，业必将迈入大数据。趋势来了：只有两年的机会，你站在哪里？数据就是以数为据可教授l 解决数据的问题，用数据解决问题；是两个不同的过程。工控界的转型：+互联网、+云千里眼用数据解决了“看”的问题， 形成云平台，是一种但是我们很少涉及“想” 的问题，数据在手而不去挖掘，就是散金子。为什么不利用数据为客户提供智能化增值服务？12:59.212:59.412:59.5

3、12:59.712:59.813:00.013:00.213:00.313:00.513:00.713:00.813:01.013:01.113:01.313:01.513:01.713:01.913:02.113:02.313:02 51 :02 713:02.913:03.113:03.34.05256.306256.25256.24255.62256.3209376.2456.24253.5056.2940633.43756.2356.306256.2943756.24256.32756.276.24253 62259.398759.3959.2359.39758.83259.40259

4、.3834389.3959.3915629.428.78759.3946889.39757.24259.39758.73759.0859 3959 3990629.4050019.48.5959.4018759.3918756.096.5143756.526.536.6156.506256.52256.53256.49756.584.2256.5371886.4981255.3256.535.646.686.9103136.8628136.8734386.836256.921256.8653136.886.82256.69254.75756.9068756.9243764.32756.8756

5、 8846876.8834386.87756.9403126.12756.8756.9356.9156.8725我只要数，不要据做挖掘分析后如何发现以往不可见的问题？做深度数据挖掘如何解除客户对数据安全的担忧？再提交只有属性无关，才是通成纯数值分工合作用的空间，665365625328136 48看右边的24行数值，能6281875.24256.334.44256.5346886.53256.616.5321876.532187又怎发现什么？24么办？6.239687换个角度去看：海量数据的特征分析我们是转过90看大家都是这么看，时间为横轴14000行数据的切片叠印大数据分析6750行数据属于

6、一种特征24行数据的叠印某自动线有71个传感器：19919行数据；没有提供数据的任何属性2014-11-27 17:35,480.17,243.66,37.32,36.19,147.91,0.009670457,5.8145E-06,0.005399653,7.76,0.000244129,6.61,4.87,3.94,3.67,0.001809726,0.0000003619 ,0.0100000000 ,0.0400000000 ,0.3,0.03,0.00001,0.000003,0.25,0.000008,0.23,0.03,-0.000015,-0.4,0.03,0.21,10,62

7、,63,64,65,66,67,19.64,17.05,13.96,10.96,12.46,11.34,0.21,11,62,63,64,65,66,67,19.1,7.71,15.73,15.37,12.46,6.32,0.21,12,63,64,65,66,67,68,2.55,1.86,18.51,3.8,11.51,7.592014-11-27 17:35,480.17,249.3,37.06,35.32,147.13,0.00947909,5.7503E-06,0.005385684,7.76,0.000245489,6.61,4.87,3.94,3.67,0.001809726,0

8、.0000003619 ,0.0100000000 ,0.0400000000 ,0.3,0.03,0.00001,0.000004,0.26,0.000008,0.24,0.03,-0.000015,-0.4,0.03,0.21,10,62,63,64,65,66,67,19.64,17.05,13.96,10.96,12.46,11.34,0.21,11,62,63 64,65,66,67,19 1,7 71,15.73,15.37,12.46,6.32,0.21,12,63,64,65,66,67,68,2.55,1.86,18.51,3.8,11.51,7.592014-11-27 1

9、7:35,478.87,243.31,35.41,35.5,148.43,0.009322503,5.6868E-06,0.005385684,7.76,0.000245489,6.61,4.87,3.94,3.68,0.001809726,0.0000003619 ,0.0100000000 ,0.0400000000 ,0.3,0.03,0.00001,0.000004 0.27,0 000009 0 23 0.0 ,-0 000015,-0.4,0.03,0.21,10,62,63,64,65,66,67,19.64,17.05,13.96,10.96,12.46 1 . 4,0 21,

10、12 62,63,64,65,66,67,19.1,7.71,15.73,15.37,12.46,6.32,0.21,13,63,64,65,66,67,68,2.55,1.86,18.51,3 8,11 5 7 59。2014-11-28 10:11,0,0,0,0,0,0.003559749,6.741E-07,0.000268911,-0.05,9.512E-07,0.02,0.1,0.11,0.01,0.000407014,0.0000008801,91.8400000000 ,0.0037800000 ,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,

11、0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,02014-11-28 10:11,0,0,0,0,0,0.003571627,6.741E-07,0.000269608,-0.05,9.512E-07,0.02,0.08,0.11,0.01,0.000374635,0.0000007368,66.7000000000 ,0.0037800000 ,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,

12、0,0,0,0,0,0,0,0,0,0,02014-11-28 10:11,0,0,0,0,0,0.003595502,6.741E-07,0.000269608,-0.05,9.512E-07,0.02,0.1,0.11,0.01,0.000374635,0.0000006168,48.4400000000 ,0.0037800000 ,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,02014-11-28 10:11,0,0,0,0

13、,0,0.003583544,6.779E-07,0.000268911,-0.05,9.512E-单行数据的可视化：快照（切片图）例如第23行数据2014-11-27 17:36,478.73,243.14,35.41,35.24,147.65,0.007117792,4.9222E-06,0.005470046,7.85,0.000244129,6.78,4.86,3.92,3.66,0.001809726,0.0000003030,0.0100000000 ,0.0400000000 ,0.29,0.03,0.00001,0.000004,0 27 0.000009,0.24,0.03,

14、-0.000015,-3.96,10.96,12.46,11.34,0.21,16,2,0.21,17,63,64,65,66,67,68,2.这行数据有71个数据元，可以看成是71维空间中的一个也可用切片图的，在一个归一化的平面上表示。一张切片图代表个瞬间状态的某一所有19919行数据的切片图叠印后的可视化：全维度分析实例从这些这19919个瞬显然，用传统的间中找规律或者找特征是很的！因此我们要把这141万多个数据放到大数据分析引擎中去处理。经过大数据引擎1小时46分钟的训练、学习，运算分析处理，发现了428种特征模式，Start Learning at:2015-03-24 21:03:0

15、1 Finish Learning at:2015-03-24 22:49:43 19919 Total number of series71 Sensor Points Per Series图形表示比如模式1、模式2用切片图形叠加的来表示：3 series fit in M 18 series fit in M 2 series fit in M 1 series fit in M 5 series fit in M 1 series fit in M 2 series fit in M 2 series fit in M 2 series fit in M 3 series fit in

16、M 3 series fit in M 2 series fit in M 4 series fit in M 1 series fit in M 2 series fit in M 3 series fit in M 1 series fit in M 6 series fit in MPatter(1) Patter(2) Patter(3) Patter(4) Patter(5) Patter(6) Patter(7) Patter(8) Patter(9) Patter(10) Patter(11) Pa ter(12) Patter(13) Patter(14) Patter(15)

17、 Patter(16) Patter(17) Patter(18)来说：我们首先关心包含数据行数多的模式，我们发现：3 series fit in M 9 series fit in M 1 series fit in M 1 series fit in M370 series fit in M 3 series fit in M 184 series fit in M 8 series fit in M 282 series fit in M 105 series fit in M 110 series fit in M 16 series fit in M 4 series fit in

18、M5 series fit in MPatter(388) Patter(389) Patter(390) Patter(391)Patter(392) Patter(393)Patter(394) Patter(395)Patter(396) Patter(397) Patter(398) Patter(399) Patter(400) Patter(401)17742 series fit in M 112 series fit in MPatter(402) Patter(403)402号模式包含17742行数据， 占总行数的89%；我们1 series fit in M 4 serie

19、s fit in M 17 series fit in M 5 series fit in MPatter(404) Patter(405) Patter(406) Patter(407)这属于的正常状态那么异常状态可能是如下几个，392号模式、396号模式等3 series fit in M 9 series fit in M 1 series fit in M 1 series fit in M370 series fit in M 3 series fit in M 184 series fit in M 8 series fit in M 282 series fit in M 105

20、 series fit in M 110 series fit in M 16 series fit in M 4 series fit in M5 series fit in MPatter(388) Patter(389) Patter(390) Patter(391)Patter(392) Patter(393)Patter(394) Patter(395)Patter(396) Patter(397) Patter(398) Patter(399) Patter(400) Patter(401)17742 series fit in M 112 series fit in MPatter(402) Patter(403)1 series fit in M 4 series fit in M 17 series fit in M 5 series fit in MPatter(404) Patter(40