Machine Predictive Maintenance Classification

### Machine Predictive Maintenance Classification Dataset

Since real predictive maintenance datasets are generally difficult to obtain and in particular difficult to publish, we present and provide a synthetic dataset that reflects real predictive maintenance encountered in the industry to the best of our knowledge.

The dataset consists of 10 000 data points stored as rows with 14 features in columns
- UID: unique identifier ranging from 1 to 10000
- productID: consisting of a letter L, M, or H for low (50% of all products), medium (30%), and high (20%) as product quality variants and a variant-specific serial number
- air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K
- process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.
- rotational speed [rpm]: calculated from powepower of 2860 W, overlaid with a normally distributed noise
- torque [Nm]: torque values are normally distributed around 40 Nm with an σ = 10 Nm and no negative values.
- tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. and a
'machine failure' label that indicates, whether the machine has failed in this particular data point for any of the following failure modes are true.

## Important : There are two Targets - Do not make the mistake of using one of them as feature, as it will lead to leakage.
- Target : Failure or Not
- Failure Type : Type of Failure

### Acknowledgements

UCI : https://archive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset

### 机器预测性维护分类数据集

鉴于实际预测性维护数据集通常难以获取，尤其是难以公开发布，我们在此提供并展示一个合成数据集，该数据集尽可能地反映了工业界所遭遇的真实预测性维护情况。

该数据集包含10,000个数据点，存储为具有14个特征的列行格式
- UID：唯一标识符，范围从1至10,000
- 产品ID：由字母L、M或H组成，分别代表低（占所有产品的50%）、中（30%）和高（20%）的产品质量变体，以及特定的序列号
- 空气温度 [K]：通过随机游走过程生成，并归一化至300 K周围的标准差为2 K
- 过程温度 [K]：通过随机游走过程生成，并归一化至标准差为1 K，叠加在空气温度上再加10 K
- 转速 [rpm]：由功率为2860 W计算得出，叠加正态分布的噪声
- 扭矩 [Nm]：扭矩值呈正态分布，围绕40 Nm，σ = 10 Nm，且无负值
- 工具磨损 [min]：质量变体H/M/L分别将5/3/2分钟的磨损时间加到加工过程中使用的工具上，并有一个'machine failure'标签，表示在特定的数据点中，机器是否因以下任一故障模式而出现故障。

## 重要提示：存在两个目标变量——请勿将其中一个作为特征使用，否则会导致信息泄露。
- 目标：故障与否
- 故障类型：故障类型

### 致谢

UCI：https://archive.ics.uci.edu/ml/datasets/AI4I+2020+Predictive+Maintenance+Dataset