观光电梯运行健康状态监测评估数据

通过采集观光电梯运行平台的实时监测数据，完成对观光电梯运行状态的综合评估，以观光电梯振动频率、运行噪声、门机运转状态、曳引系统性能、平层精度、制动器工作状态等因素为自变量，综合分析观光电梯的健康指数。观光电梯智能监控平台帮助管理方更好地掌握电梯运行状况，及时发现潜在故障隐患，并提供预测性维护建议和预警支持。本数据适用于物业管理方和维保单位：为客户提供了一站式的电梯管理解决方案，无论是日常监测、故障诊断还是维保计划，通过该指数为电梯提供个性化的健康评价指标，确保电梯安全平稳运行。通过观光电梯物联网平台采集电梯运行数据，完成对电梯设备的实时监测和健康评估。将数据预处理后输入随机森林模型，根据各监测指标的状态值计算出电梯运行健康指数。(1)健康指数计算：使用随机森林模型计算健康指数，计算公式为：健康指数 = (1/N) * ∑(i=1 to N) Ti(x)其中：N是随机森林中决策树的数量，N设置为100；Σ(i=1 to N)表示从1到N的求和；Ti(x)是第i个决策树对输入x的预测输出；x是输入的11维特征向量，对应以下11个监测指标，包括：（门机电流(A)、曳引机电流(A)、振动(mm/s)、平层精度(mm)、噪音(dB)、制动器间隙(mm)、导轨导靴间隙(mm)、钢丝绳磨损(%)、轿厢载重(kg)、运行速度(m/s)、日运行次数(次)）。（2）健康状态评估：根据计算得出的0-100健康指数，将电梯运行状态划分为五个等级：1）优秀：健康指数 ≥95；2）良好：85≤ 健康指数 <95；3）正常：75≤ 健康指数 <85；4）注意：65≤ 健康指数 <75；5）警告：健康指数 <65。

This dataset is developed by collecting real-time monitoring data from the operation platforms of sightseeing elevators to perform comprehensive assessment of their operating states. Taking factors such as vibration frequency, operating noise, hoistway door motor operating status, traction system performance, landing leveling accuracy, and brake working condition of sightseeing elevators as independent variables, a comprehensive analysis is conducted to derive the elevator health index.

The intelligent sightseeing elevator monitoring platform assists property managers in better mastering elevator operating conditions, timely detecting potential fault hazards, and providing predictive maintenance recommendations and early warning support.

This dataset is applicable to property management companies and maintenance enterprises. It offers customers a one-stop elevator management solution, including daily monitoring, fault diagnosis and maintenance planning. By utilizing the health index, personalized health evaluation metrics are provided for elevators to ensure their safe and stable operation.

The dataset collects elevator operating data through the sightseeing elevator IoT platform to realize real-time monitoring and health assessment of elevator equipment. After data preprocessing, the data is input into the Random Forest model to calculate the elevator operating health index based on the status values of each monitoring indicator.

(1) Health Index Calculation
The health index is calculated using the Random Forest model, with the formula specified as:
$$ ext{Health Index} = frac{1}{N} imes sum_{i=1}^{N} T_i(x)$$
Where:
- $N$ is the number of decision trees in the Random Forest, set to 100;
- $sum_{i=1}^{N}$ denotes the summation from 1 to $N$;
- $T_i(x)$ is the predicted output of the $i$-th decision tree for input $x$;
- $x$ is an 11-dimensional input feature vector corresponding to the following 11 monitoring indicators: (hoistway door motor current (A), traction machine current (A), vibration (mm/s), landing leveling accuracy (mm), noise (dB), brake clearance (mm), guide rail and guide shoe clearance (mm), wire rope wear (%), car load (kg), operating speed (m/s), daily operating times).

(2) Health Status Assessment
Based on the calculated health index ranging from 0 to 100, the elevator operating status is divided into five levels:
1) Excellent: Health Index ≥ 95;
2) Good: 85 ≤ Health Index < 95;
3) Normal: 75 ≤ Health Index < 85;
4) Caution: 65 ≤ Health Index < 75;
5) Warning: Health Index < 65.