工业设备健康运行智能监测数据

在旋转类及往复式工业现场设备上安装无线或者有线的传感器（包括但不限于：振动、转速、电流、温度、压力等），然后通过边缘智能处理单元，提取特征参数，并发送到云端，通过智能算法研判设备的本质运行健康状态，以及对设备问题的故障判断，可以给与设备运维部门更加智能化的指导，提高设备的生产效率，降低设备的运维费用，并提高生产场所的安全。这个领域是工业数字化智能化道路上必须要做的一块工作，也是工业经济发展和现代化过程中极其重要的一环。1.数据采集：通过高精度的数据采集系统，采集工业设备的振动信号。
2.数据处理：对采集到的原始数据进行处理，通过异常值判断算法剔除非正常的传感器数据，并计算预设的特征参数，如峰值、有效值、峭度等。
3.数据分析：处理后的原始数据和特征参数集，传递给“智能诊断算法”深度学习模型，“智能诊断算法”是基于深度残差网络（Residual Network，ResNet）的故障诊断算法模块。该算法首先采集多通道振动信号数据，然后提取各通道振动信号的时域、频域特征，接着利用距离评估技术优选敏感特征，剔除冗余特征，最后以深度ResNet替代诊断专家进行自动故障诊断，并将各通道故障诊断结果进行加权集成，得到最终诊断结果。
4.数据应用：通过对工业设备状态相关的各类型传感器信号和控制器信息的搜集和处理，综合判断设备的运行健康状态，以及对设备问题的故障判断，可以给与设备运维部门更加智能化的指导，提高设备的生产效率，降低设备的运维费用，并提高生产场所的安全。这个领域是工业数字化智能化道路上必须要做的一块工作，也是工业经济发展和现代化过程中极其重要的一环。

Wireless or wired sensors (including but not limited to: vibration, rotational speed, current, temperature, pressure, etc.) are installed on rotating and reciprocating industrial field equipment. Then, feature parameters are extracted via edge intelligent processing units and sent to the cloud. Through intelligent algorithms, the essential operational health status of the equipment and fault diagnosis for equipment issues can be assessed, providing more intelligent guidance for equipment operation and maintenance departments, improving equipment production efficiency, reducing equipment operation and maintenance costs, and enhancing the safety of production sites. This field is an indispensable part of the industrial digitalization and intelligentization path, and also a crucial link in industrial economic development and modernization.
1. Data Acquisition: Collect vibration signals of industrial equipment via high-precision data acquisition systems.
2. Data Processing: Process the collected raw data, filter out abnormal sensor data using outlier detection algorithms, and calculate pre-set feature parameters such as peak value, root mean square (RMS) value, kurtosis, etc.
3. Data Analysis: The processed raw data and feature parameter sets are input into the deep learning model of the "Intelligent Diagnosis Algorithm", which is a fault diagnosis algorithm module based on Residual Network (ResNet). The algorithm first collects multi-channel vibration signal data, then extracts time-domain and frequency-domain features of vibration signals from each channel, then uses distance evaluation technology to select sensitive features and eliminate redundant features, finally uses deep ResNet to replace diagnostic experts for automatic fault diagnosis, and performs weighted integration of fault diagnosis results from each channel to obtain the final diagnosis result.
4. Data Application: Collect and process various types of sensor signals and controller information related to the operating status of industrial equipment, comprehensively judge the operational health status of the equipment and conduct fault diagnosis for equipment problems, provide more intelligent guidance for equipment operation and maintenance departments, improve equipment production efficiency, reduce equipment operation and maintenance costs, and enhance the safety of production sites. This field is an indispensable part of the industrial digitalization and intelligentization path, and also a crucial link in industrial economic development and modernization.

搜集汇总

数据集介绍

特点

该数据集为工业设备健康运行智能监测数据，包含12个字段，规模102条，每分钟更新，用于通过智能算法研判设备运行状态和故障，提高生产效率和安全性。数据处理采用高精度采集系统和深度残差网络算法。