破碎设备智能故障预测数据

该数据在工业破碎设备故障预测中具有重要的应用价值。能够提供自动化设备健康监测，更精确地判断破碎设备的潜在故障风险，帮助设备维护人员进行预防性维护与生产调度。在智能工厂设备管理系统中具有广泛的应用场景，特别是在矿山开采设备监控、建筑废料处理设备管理和化工原料破碎设备维护中，能够提高设备运行可靠性，降低非计划停机损失，避免因设备突发故障导致的生产线停产，提供智能化的设备维护决策支持。通过实时监测振动、温度、电流等关键运行参数，该评估体系能够在设备出现异常征象时及时预警，使维护团队能够在最佳时间窗口内安排维修作业，既保证了生产连续性，又最大化了设备使用寿命，为企业实现精益化设备管理和降本增效提供了有力的数据支撑。1. 数据收集和预处理
（1）数据收集
通过传感器实时采集破碎设备运行数据，包括监测时间、设备型号、累计运行时长（小时）、实时振动值（mm/s）、轴承温度（℃）、运行电流（A）、负载率（%）等关键参数。
（2）数据预处理
对采集的原始数据进行滤波去噪、异常值检测和数据补全处理，确保数据质量和完整性。
2. 异常度评分转换计算
（1）振动异常度评分
振动异常度评分 = max(0, min(10, (实时振动值-2.0)/6.0 × 10))
（2）温度异常度评分
温度异常度评分 = max(0, min(10, (轴承温度-65)/20 × 10))
（3）电流异常度评分
电流异常度评分 = max(0, min(10, abs(运行电流-160)/100 × 10))
（4）运行时长系数计算
运行时长系数 = min(10, 累计运行时长/8760 × 10)
（5）负载异常度评分
负载异常度评分 = max(0, min(10, abs(负载率-75)/25 × 10))
3. 故障风险综合评分计算
故障风险综合评分 = 振动异常度评分×权重(0.30) + 温度异常度评分×权重(0.25) + 电流异常度评分×权重(0.20) + 运行时长系数×权重(0.15) + 负载异常度评分×权重(0.10)
4. 设备健康等级预警分级
（1）风险等级划分
根据故障风险综合评分进行四级分类：
正常：0 ≤ 故障风险综合评分 < 3
注意：3 ≤ 故障风险综合评分 < 6
预警：6 ≤ 故障风险综合评分 < 8
危险：8 ≤ 故障风险综合评分 ≤ 10

This dataset holds significant application value in fault prediction for industrial crushing equipment. It enables automated equipment health monitoring, more accurate judgment of potential fault risks of crushing equipment, and assists equipment maintenance personnel in carrying out preventive maintenance and production scheduling. It has a wide range of application scenarios in intelligent factory equipment management systems, especially in mining equipment monitoring, construction waste treatment equipment management, and chemical raw material crushing equipment maintenance. It can improve equipment operation reliability, reduce unplanned downtime losses, avoid production line shutdowns caused by sudden equipment failures, and provide intelligent equipment maintenance decision support. By real-time monitoring of key operating parameters such as vibration, temperature, and current, this evaluation system can issue timely warnings when abnormal signs appear in the equipment, allowing the maintenance team to arrange maintenance operations within the optimal time window. This not only ensures production continuity but also maximizes equipment service life, providing strong data support for enterprises to achieve lean equipment management and cost reduction and efficiency improvement.

1. Data Collection and Preprocessing
(1) Data Collection
Operating data of crushing equipment is collected in real-time via sensors, including key parameters such as monitoring time, equipment model, cumulative operating duration (hours), real-time vibration value (mm/s), bearing temperature (℃), operating current (A), and load rate (%).
(2) Data Preprocessing
Filtering and denoising, outlier detection, and data completion are performed on the collected raw data to ensure data quality and integrity.

2. Abnormality Score Conversion and Calculation
(1) Vibration Abnormality Score
Vibration Abnormality Score = max(0, min(10, (real-time vibration value - 2.0)/6.0 × 10))
(2) Temperature Abnormality Score
Temperature Abnormality Score = max(0, min(10, (bearing temperature - 65)/20 × 10))
(3) Current Abnormality Score
Current Abnormality Score = max(0, min(10, abs(operating current - 160)/100 × 10))
(4) Operating Duration Coefficient Calculation
Operating Duration Coefficient = min(10, cumulative operating duration / 8760 × 10)
(5) Load Abnormality Score
Load Abnormality Score = max(0, min(10, abs(load rate - 75)/25 × 10))

3. Comprehensive Fault Risk Score Calculation
Comprehensive Fault Risk Score = Vibration Abnormality Score × Weight (0.30) + Temperature Abnormality Score × Weight (0.25) + Current Abnormality Score × Weight (0.20) + Operating Duration Coefficient × Weight (0.15) + Load Abnormality Score × Weight (0.10)

4. Equipment Health Level Early Warning Classification
(1) Risk Level Division
Classify into four levels based on the comprehensive fault risk score:
Normal: 0 ≤ Comprehensive Fault Risk Score < 3
Caution: 3 ≤ Comprehensive Fault Risk Score < 6
Early Warning: 6 ≤ Comprehensive Fault Risk Score < 8
Danger: 8 ≤ Comprehensive Fault Risk Score ≤ 10

搜集汇总

背景与挑战

背景概述

该数据集为破碎设备智能故障预测数据，包含541条记录，每日更新，涵盖振动、温度、电流等运行参数及异常度评分，用于通过加权算法计算故障风险综合评分，实现设备健康等级预警。其特点在于提供工业破碎设备的预防性维护支持，适用于矿山、建筑等场景，帮助降低非计划停机损失和提高设备可靠性。