Months-in-service Year of Production Date Running Sum of RateCalculated 1 2021 0.000038 2 2022 0.000068 2 2021 0.000567 2 2020 0.000206 2 2019 0.000071 2 2018 0.000372 3 2021 0.000795 3 2020 0.000371 3 2019 0.000250 3 2018 0.000446 4 2022 0.000204 4 2021 0.001301 4 2020 0.001471 4 2019 0.000607 4 2018 0.000818 5 2022 0.000339 5 2021 0.001904 5 2020 0.002226 5 2019 0.001321 5 2018 0.002603 6 2021 0.002157 6 2020 0.002941 6 2019 0.002107 6 2018 0.003644 7 2022 0.000407 7 2021 0.002394 7 2020 0.003147 7 2019 0.002892 7 2018 0.006173 8 2021 0.002753 8 2020 0.003601 8 2019 0.003606 8 2018 0.008032 9 2021 0.003122 9 2020 0.004020 9 2019 0.004177 9 2018 0.010115 对这个列表中用Months-in-service和Running Sum of RateCalculated这两列形成的图像 利用weibull模型提供可靠性评估和预测分析的代码, 拟合出预测曲线
时间: 2024-01-04 09:04:34 浏览: 19
您可以使用Python中的scipy库来拟合Weibull模型并生成预测曲线。首先,您需要安装scipy库(如果尚未安装):
```
pip install scipy
```
然后,您可以使用以下代码来拟合Weibull模型并生成预测曲线:
```python
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
# 定义Weibull函数
def weibull(x, alpha, beta):
return 1 - np.exp(-(x/alpha)**beta)
# 数据
months_in_service = [1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9]
running_sum_of_rate = [0.000038, 0.000068, 0.000567, 0.000206, 0.000071, 0.000372,
0.000795, 0.000371, 0.000250, 0.000446, 0.000204, 0.001301,
0.001471, 0.000607, 0.000818, 0.000339, 0.001904, 0.002226,
0.001321, 0.002603, 0.002157, 0.002941, 0.002107, 0.003644,
0.000407, 0.002394, 0.003147, 0.002892, 0.006173, 0.002753,
0.003601, 0.003606, 0.008032, 0.003122, 0.004020, 0.004177, 0.010115]
# 拟合Weibull模型
params, cov = curve_fit(weibull, months_in_service, running_sum_of_rate)
# 提取拟合参数
alpha = params[0]
beta = params[1]
# 生成预测曲线
x = np.linspace(min(months_in_service), max(months_in_service), 100)
y_pred = weibull(x, alpha, beta)
# 绘制图像
plt.scatter(months_in_service, running_sum_of_rate, label='Data')
plt.plot(x, y_pred, label='Weibull Fit')
plt.xlabel('Months in Service')
plt.ylabel('Running Sum of Rate Calculated')
plt.title('Weibull Model Fit')
plt.legend()
plt.show()
```
运行以上代码后,您将获得一个包含数据点和Weibull模型拟合曲线的图表,以评估可靠性并进行预测分析。