台州温岭地区流量计异常检测数据

流量计的异常检测预警系统旨在提升系统安全性、降低维护成本，并确保设备的稳定运行。通过结合DBSCAN聚类算法和高斯混合模型（GMM），该算法能够有效分析流量、压力、温度等数据，识别设备的常规工作模式，并发现偏离常规的数据点。这有助于及时发现系统中的潜在异常，如设备故障、传感器异常或外部干扰，实现更科学和系统化的监控和维护预警，为决策者提供精准的维护策略支持。1. 数据采集：通过流量计采集系统获取关键字段，包括：省份、地市、区域、流量计编码、瞬时标况流量、标况流量、温度、出口压力（kPa）、以及采集时间。
2. 数据预处理：在指定区域内，按照流量计类型对数据进行分类，并对瞬时标况流量、标况流量、温度、出口压力数据进行标准化处理。
3. 聚类分析：使用DBSCAN识别各流量计在不同温度和流量条件下的工作模式。通过合理设置邻域最大距离和簇内所需的最小样本数，将数据划分为多个簇，同时将无法归类为任何簇的数据点标记为噪声。
4. 模型训练：对DBSCAN识别出的非噪声数据，进行GMM模型训练。通过合理设置GMM的簇数量、协方差类型、初始化方法、收敛阈值、先验权重等，进行模型调优训练。
5. 异常检测：对每个数据点应用GMM计算出其在各个簇中的概率，并取最大概率值。设定一个概率阈值0.93，如果该数据点最大概率值小于概率阈值0.93，则标记为异常。

The anomaly detection and early warning system for flow meters aims to improve system safety, reduce maintenance costs, and ensure stable equipment operation. By combining the DBSCAN clustering algorithm and the Gaussian Mixture Model (GMM), this system can effectively analyze data such as flow, pressure and temperature, identify the normal working patterns of equipment, and detect data points deviating from the norm. This helps to timely discover potential anomalies in the system, including equipment failures, sensor abnormalities or external interference, realize more scientific and systematic monitoring, maintenance and early warning, and provide decision-makers with accurate maintenance strategy support.
1. Data Collection: Key fields are collected via the flow meter acquisition system, including: province, city, district, flow meter code, instantaneous standard-condition flow rate, standard-condition flow rate, temperature, outlet pressure (kPa), and collection time.
2. Data Preprocessing: Within the specified area, data is classified by flow meter type, and the data of instantaneous standard-condition flow rate, standard-condition flow rate, temperature and outlet pressure are standardized.
3. Clustering Analysis: DBSCAN is used to identify the working modes of each flow meter under different temperature and flow conditions. By properly setting the maximum neighborhood distance and the minimum number of samples required within a cluster, the data is divided into multiple clusters, while data points that cannot be classified into any cluster are marked as noise.
4. Model Training: GMM model training is conducted on the non-noise data identified by DBSCAN. Model tuning and training are carried out by properly setting the number of clusters, covariance type, initialization method, convergence threshold, prior weights and other parameters of GMM.
5. Anomaly Detection: For each data point, calculate its probability in each cluster using GMM and take the maximum probability value. A probability threshold of 0.93 is set, and if the maximum probability value of the data point is less than 0.93, it is marked as abnormal.

搜集汇总

数据集介绍

特点

该数据集为台州温岭地区流量计异常检测数据，包含1000条记录，每日更新，用于流量计的异常检测预警系统。数据字段涵盖流量、压力、温度等关键信息，结合DBSCAN聚类算法和高斯混合模型（GMM）进行异常检测。