# 工业方向

机器学习在制造工业中的应用主要包括**质量预测**、**设备维护(PHM)**、**供需预测**、**计划排产(scheduling)**、**参数优化**等方面。

* 时域：除了[时间序列](../an-li-1/ji-qiao)中介绍的，还包括peak2peak等
* 频域：傅里叶变换 ，均方根，波形，脉冲

## 故障预测与健康管理

通过预测性维护达到成本与安全风险的最优。

* [DF-风机叶片开裂故障预警](https://www.datafountain.cn/competitions/302)
  * 2nd [code](https://github.com/SY575/DF-Early-warning-of-the-wind-power-system)
  * 4th [code](https://github.com/whoami-zy/fault-forecast)
  * x [code](https://github.com/iszeke/wind-turbine-phm)
* [DC-轴承故障检测训练赛](https://js.dclab.run/v2/cmptDetail.html?id=248)
  * x [code](https://github.com/luanshiyinyang/DataMiningProject/tree/Bearing)
  * x [code](https://github.com/zhangxiaoling/Bearing-fault-detection)
  * x [code](https://github.com/xiaosongshine/bearing_detection_by_conv1d)
* [科大讯飞工程机械核心部件寿命预测挑战赛](http://challenge.xfyun.cn/2019/gamedetail?blockId=981)
  * 1st [discussion](https://zhuanlan.zhihu.com/p/82394380)
  * 2nd [code](https://github.com/pinlank/kdxf2019top2) [discussion](https://zhuanlan.zhihu.com/p/91840878)
  * baseline [code](https://github.com/woxima/2019iFLYTEK_MachineryLife_Baseline/blob/master/baseline.py)
* [kaggle-vsb-power-line-fault-detection](https://www.kaggle.com/c/vsb-power-line-fault-detection)
  * 1st [disucssion](https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/87038)
  * 2nd [discussion](https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/86616)
  * 6th [discussion](https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/85170)
  * 13rd [code](https://github.com/MaxHalford/kaggle-vsb-power)
  * baseline [code](https://github.com/randxie/Kaggle-VSB-Baseline)
* [工业大数据-风机叶片结冰预测大赛](http://www.industrial-bigdata.com/Competition)
  * base [code](https://github.com/zhbink/2017-IndustryBigData)
  * base [code](https://github.com/XinArkh/Industrial_BigData_Competition_2017)
  * base [code](https://github.com/minelabwot/phmlearn)
* [工业大数据-风机齿型带故障分类大赛](http://www.industrial-bigdata.com/Competition)
* [工业大数据-刀具剩余寿命预测](http://www.industrial-bigdata.com/Competition)
  * base [code](https://github.com/Bingohong/2018-Industrial-Bigdata)
* [工业大数据-第三届](gong-ye-fang-xiang)
  * [base](https://github.com/ruyueshuo/Prognosis-of-Rotor-Parts-Fly-off)
  * [base](https://github.com/MatthewSZhang/partsflyoff)
* [华为云-网络人工智能黑客松大赛的硬盘异常检测](gong-ye-fang-xiang)
  * 1st [discussion](https://mp.weixin.qq.com/s/LEsJvrB4V3YyOAZP-PGLFA)
* [天池-PAKDD2020](https://tianchi.aliyun.com/competition/entrance/231775/introduction)
* [天池-PAKDD2021](https://tianchi.aliyun.com/competition/entrance/531874/introduction)
  * 6th [discussion](https://tianchi.aliyun.com/notebook-ai/detail?postId=222291)
  * 15th [code](https://github.com/eggachecat/pakdd-2021)
  * x [code](https://github.com/strayberry/pakdd_qi_training_tenth_level)
* [AIops竞赛](gong-ye-fang-xiang)
* [PHM data challenge](gong-ye-fang-xiang)
  * 2015 [data](https://github.com/robot007/PHM15)
  * 2016 [code](https://github.com/allen050883/2016-PHM-challenge-society) [code](https://github.com/patanijo/PHM2016)
  * 2017 [code](https://github.com/RyanWangCode/PHM)
  * 2018 [code](https://github.com/ninja1mmm/2018-phm-data-challenge)
* [WAIC全球工业互联网算法大赛](gong-ye-fang-xiang)
* [2019数字中国创新大赛 混凝土泵车砼活塞故障预警](gong-ye-fang-xiang)
  * 1st [code](https://github.com/pinlank/DCIC_Failure-Prediction-of-Concrete-Piston-for-Concrete-Pump-Vehicles_1st)
  * 2nd [code](https://github.com/PanJianning/DCIC-2019-HNT-2th-Place)
* [风电异常数据识别与清洗竞赛](gong-ye-fang-xiang)
  * 1st [discussion](https://zhuanlan.zhihu.com/p/425439477)

## 质量预测

### 参数缺陷

* [Bosch Production Line Performance](https://www.kaggle.com/c/bosch-production-line-performance/overview)
  * 1st [discussion](https://www.kaggle.com/c/bosch-production-line-performance/discussion/25434)
* [Mercedes-Benz Greener Manufacturing](https://www.kaggle.com/c/mercedes-benz-greener-manufacturing)
* [PAKDD 2014 - ASUS Malfunctional Components Prediction](https://www.kaggle.com/c/pakdd-cup-2014)
* [第四届工业大数据-注塑机产品质量](http://www.industrial-bigdata.com/Competition)
  * 1st [discussion](https://github.com/LongxingTan/Data-competitions/tree/master/industry-injection-molding)
  * 5th [code](https://github.com/chuangwang1991/VirtualMeasurement_molding)
  * 9th [code](https://github.com/Xinchengzelin/InjectionMoulding)
* [天池工业AI大赛-智能制造质量预测](https://tianchi.aliyun.com/competition/entrance/231633/introduction)
  * 2nd [code](https://github.com/luoda888/2018-Industrial-Big-Data-Top2-Solution)
* [天池-津南数字制造算法挑战赛](https://tianchi.aliyun.com/competition/entrance/231695/introduction)
* [天池-工业4.0大数据竞赛 - 制造业质量控制](gong-ye-fang-xiang)
  * 1st [code](https://github.com/wepe/TianChi-Industry4.0-Competition)
* [DF-西门子离散制造过程中典型工件的质量符合率预测](https://www.datafountain.cn/competitions/351)
  * 1st [code](https://github.com/CcIsHandsome/-TOP1-) [discussion](https://zhuanlan.zhihu.com/p/100358795)
* [基于大数据的汽轮机瞬态应力分析与预测](https://www.datafountain.cn/competitions/301)
* [CCF大数据与计算智能大赛-工件检测](gong-ye-fang-xiang)
  * 1st [code](https://github.com/CcIsHandsome/-TOP1-)
* [biendata-INSPEC工业大数据质量预测赛](gong-ye-fang-xiang)

### 视觉缺陷

* [天池-雪浪制造AI挑战赛—视觉计算辅助良品检测](https://tianchi.aliyun.com/competition/entrance/231666/introduction)
* [2018广东工业智造大数据创新大赛—智能算法赛](https://tianchi.aliyun.com/competition/entrance/231682/information)
* [2021广东工业智造创新大赛—智能算法赛](https://tianchi.aliyun.com/competition/entrance/531846/introduction)

## 供需

* [光伏发电量预测](gong-ye-fang-xiang)
  * 1st [discussion](https://zhuanlan.zhihu.com/p/44755488)
  * 25th [code](https://github.com/jsnuwjl/Photovoltaic_power)
* [工业大数据-水电站流量](http://www.industrial-bigdata.com/Competition)
  * 1st [code](https://github.com/whoami-zy/WaterFlow)
* [天池菜鸟-需求预测与分仓规划](gong-ye-fang-xiang)
  * 6th [code](https://github.com/wepe/CaiNiao-DemandForecast-StoragePlaning)
  * 10th [code](https://github.com/xing89qs/TianChi_CaiNiao_Season2)

## 优化-排产调度

* [讯飞-数字化车间智能排产调度挑战赛](https://blog.csdn.net/Lyyyp/article/details/126503338?spm=1001.2014.3001.5502)
  * [答辩回看](https://1024.iflytek.com/liveroom?date=1117\&id=1.2_%E5%A4%A7%E8%B5%9B_56)
  * [4th](https://gitee.com/time_love/Digital-Workshop-Intelligent-Scheduling-Scheduling-Challenge/tree/master)
  * [base](https://blog.csdn.net/Lyyyp/article/details/127833677?spm=1001.2014.3001.5502)
* [雪浪云-吉利](https://www.xuelangyun.com/#/cdc)
  * 1st [code](https://github.com/LXlearning/Xuelangyun2022-automobile-production-scheduling-opt-TOP1-solution)
* [aistudio-广汽](https://aistudio.baidu.com/competition/detail/950/0/introduction)
  * base [code](https://aistudio.baidu.com/projectdetail/6557807)
* [天池调度](https://tianchi.aliyun.com/competition/entrance/531831/information)

## Reference

* https://zhuanlan.zhihu.com/p/52511837
* https://github.com/hustcxl?tab=stars
* https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark
* Industrial.Applications.of.Machine.Learning.2018.12
* https://www.seeq.com/product/overview
* spark cognition
* Dataiku
