广油工业旋转大机组故障诊断样本数据库

面向工业大机组故障诊断与维护的智能监测与预警系统，用于大型工业旋转机械，涵盖烟机、汽轮机、压缩机、发电机等多种类型的机组的实验数据研究。1、用途：能够用于大型工业旋转机械的故障诊断，包括早期故障预警、故障类型识别、故障严重程度评估等。2、起到的作用/解决的问题：大型机组故障诊断困难，依靠人工经验判断故障原因和部位，效率比较低下且成本高。利用该数据集研究构建泛化效果更好的智能诊断模型，可提高故障诊断的效率和准确性，降低维修成本，减少停机时间。3、带来的经济收益：据统计，工业大机组停机损失每天可达数十万至数百万元。利用该数据集构建的智能诊断系统，可以提前发现潜在故障并预警，从而避免计划外的停机，每年为工业大机组带来更大的经济效益。此外，通过数据驱动的智能维护，可减少人工成本，提高设备使用寿命，降低备件库存成本。随着工业互联网和智能制造的发展，该系统将具有广泛的应用前景和商业价值。

Intelligent Monitoring and Early Warning System for Fault Diagnosis and Maintenance of Large Industrial Units: This system targets large-scale industrial rotating machinery, and covers experimental data research on various types of units including flue gas turbines, steam turbines, compressors, generators, etc.
1. Applications: It can be used for fault diagnosis of large industrial rotating machinery, including early fault warning, fault type identification, fault severity assessment and other related tasks.
2. Functions and Addressed Problems: Fault diagnosis of large-scale industrial units faces great challenges, as it traditionally relies on manual expertise to judge fault causes and locations, resulting in low efficiency and high costs. Researching and constructing intelligent diagnosis models with better generalization performance using this dataset can improve the efficiency and accuracy of fault diagnosis, reduce maintenance costs, and shorten downtime.
3. Economic Benefits: According to statistics, the downtime loss of large industrial units can reach hundreds of thousands to millions of yuan per day. The intelligent diagnosis system built based on this dataset can detect potential faults in advance and issue early warnings, thereby avoiding unplanned downtime and bringing greater annual economic benefits to large industrial units. In addition, data-driven intelligent maintenance can reduce labor costs, extend equipment service life, and lower spare parts inventory costs. With the development of the Industrial Internet and intelligent manufacturing, this system will have broad application prospects and commercial value.

搜集汇总

数据集介绍

特点

该数据集是一个用于工业大机组故障诊断的样本数据库，包含2109488条数据样本，涵盖正常数据和8类故障数据。数据来源于多传感器采集的振动信号，以HDF5格式存储，适用于智能监测与预警系统的研究，能够提高故障诊断的效率和准确性，降低维修成本。