The CSTH Dataset

The CSTH Simulated Benchmark Dataset is designed for time series classification and fault detection and diagnosis (FDD). It is generated using the continuous stirred tank heater (CSTH) simulation model (https://zenodo.org/records/10093059). The model represents a heating system in which hot and cold water are mixed, heated by steam, and regulated through a closed-loop control system. The dataset consists of 9,000 multivariate time series samples, each with 200 time steps and three process variables (cold water flow, tank level, and temperature). It includes both normal operating conditions (Y=0) and faulty scenarios (Y=1), where faults are introduced through instrumentation errors. The dataset is split into: i) train.pt (70%, 6,300 samples), ii) val.pt (10%, 900 samples), and iii) test.pt (20%, 1,800 samples). This dataset is processed and ready for machine learning applications.

CSTH 仿真基准数据集专为时序分类与故障检测与诊断（Fault Detection and Diagnosis, FDD）任务设计。该数据集基于连续搅拌槽加热器（continuous stirred tank heater, CSTH）仿真模型生成，模型开源地址为https://zenodo.org/records/10093059。该模型表征一套加热系统：冷热流体混合后由蒸汽加热，并通过闭环控制系统实现运行调节。数据集共包含9000组多变量时序样本，每组样本含200个时间步与3个过程变量：冷水流量、槽罐液位与温度。数据集涵盖正常运行工况（标签Y=0）与故障场景（标签Y=1），其中故障通过仪表误差引入。数据集划分为三部分：i）训练集train.pt（占比70%，共6300个样本），ii）验证集val.pt（占比10%，共900个样本），iii）测试集test.pt（占比20%，共1800个样本）。该数据集已完成预处理，可直接用于机器学习相关应用。

搜集汇总

背景与挑战

背景概述

The CSTH Dataset是一个用于时间序列分类和故障检测与诊断（FDD）的模拟基准数据集，包含9,000个多变量时间序列样本，每个样本有200个时间步和三个过程变量。数据集分为训练集、验证集和测试集，适用于机器学习应用。