# CLFD

**Repository Path**: csc105/IFAC2023

## Basic Information

- **Project Name**: CLFD
- **Description**: 基于自监督对比学习的故障诊断算法
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 3
- **Forks**: 2
- **Created**: 2021-10-15
- **Last Updated**: 2025-07-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CLFD Project

Industrial Fault Detection using Contrastive Representation Learning on Time-series Data

https://www.sciencedirect.com/science/article/pii/S2405896323018645

# 项目简介
Deep learning (DL) has been known as one of the effective techniques for building data-driven fault detection methods. The successful DL-based methods require the condition that massive labeled data are available, but this is sometimes an inevitable obstacle in real industrial environments. As one of the solutions, autoencoders (AEs) are widely adopted since AEs can extract features from unlabeled data. However, some challenges in AE-based fault detection methods remain, such as the design of encoder architecture, the computational cost, and the usage of the limited labeled data. This paper proposes a new industrial fault detection method through learning instance-level representation of time-series based on the self-supervised contrastive learning framework (SSCL). The proposed method uses dilated-causal-convolution-based encoder-only architecture to extract the information from industrial time-series data. A new data augmentation method for time-series data is proposed based on the temporal distance distribution, which is used to construct positive pairs in SSCL. Moreover, the encoder is alternately trained by the new weighted contrastive loss and the traditional classification loss. Finally, the experiments are conducted on the industrial data set and a semi-physical system, showing the effectiveness of the proposed method.

# 项目负责人与项目复现验证人

负责人：张可鑫

复现人：芦正明

复现时间：2天

# 项目部署软硬件环境
硬件：CPU或GPU环境均可

软件：Python3.8, Pytorch1.8,scikit-learn,pandas,numpy,matplotlib等常用机器学习包

# 项目代码仓库的目录结构
- analysis/: 实验分析代码
- data/: 实验数据（可在网盘年度总结材料中下载）
- experiments/: 实验运行代码
- model/: 模型核心代码
- utils/: 其他代码
- main_exp.py 主运行函数
- main_segmentation.py 数据分割代码
- options.py 超参数代码

# 项目代码运行方式
python -m main_exp

# 项目代码预期运行结果
详见论文

# 复现小结

本项目基于自监督对比学习，提出了一种时间序列数据的表征学习方法，同时引入了一种新的数据增强方法来提高模型所学特征的鲁棒性，最终成功应用在工业故障诊断领域中。

# 该项工作还可以从以下几个方面进行拓展：
1. 数据增强的方法还可以进一步优化改进
2. 如何解决自监督对比学习中的采样偏差和表征偏差
3. 在更广泛的数据集上进行验证