设备健康度评价相关测试源码

qq_3727927916.71KB需要积分:1

资源文件列表:

HealthAssess 1.zip 大约有26个文件

HealthAssess/
HealthAssess/.DS_Store 6KB
HealthAssess/.idea/
HealthAssess/.idea/.gitignore 182B
HealthAssess/.idea/.name 12B
HealthAssess/.idea/HealthAssess.iml 440B
HealthAssess/.idea/inspectionProfiles/
HealthAssess/.idea/inspectionProfiles/profiles_settings.xml 174B
HealthAssess/.idea/misc.xml 185B
HealthAssess/.idea/modules.xml 278B
HealthAssess/.idea/other.xml 233B
HealthAssess/.idea/pythonProject.iml 221B
HealthAssess/.idea/workspace.xml 20.51KB
HealthAssess/health_assess.py 9.81KB
HealthAssess/health_assess.py.zip 2.84KB
HealthAssess/health_test.py 4.83KB
HealthAssess/main/
HealthAssess/main/__init__.py 487B
HealthAssess/main/copydata.py 374B
__MACOSX/
__MACOSX/HealthAssess/
__MACOSX/HealthAssess/._.DS_Store 120B
__MACOSX/HealthAssess/._health_assess.py 176B
__MACOSX/HealthAssess/._health_test.py 176B
__MACOSX/HealthAssess/main/
__MACOSX/HealthAssess/main/._copydata.py 176B

资源介绍:

设备健康度评价相关测试源码

# -*-coding:utf-8-*- # 设备健康度评价算法 import json import numpy as np import math # 归一化Normalized-健康度评价 from numpy import array, around np.set_printoptions(precision=4, suppress=False) # 标准化基于指标类型计算 def normalization(minValue, maxValue, tandardValue, value, type): if minValue == None or maxValue == None or tandardValue == None or value == None or type == None: return None # 标准值指标 if type == 3: if minValue <= value < tandardValue: return (value - minValue) / (tandardValue - minValue) elif tandardValue <= value <= maxValue: return (maxValue - value) / (maxValue - tandardValue) else: return 0.0 # 正向指标(越大越好) elif type == 2: health_value = (value - minValue) / (maxValue - minValue) if health_value > 1: health_value = 1 return health_value # 逆向指标(越小越好) elif type == 1: health_value = 1 - (value - minValue) / (maxValue - minValue) if health_value > 1: health_value = 1 return health_value # 指标健康度 def indicator_health(args): data_array = array(args['data']) rows = data_array.shape[0] health_list = [] for i in range(0, rows): # 指标数据（最小阈值，最大阈值，标准值，测量值,报警值,指标类型） health = normalization(data_array[i][0], data_array[i][1], data_array[i][2], data_array[i][3], data_array[i][5]) # print('指标测量数据：{0}----{1}'.format(data_array[i], health)) if health == None: health_list.append(None) else: if health < 0: health = 0 health_list.append(round(health, 4)) print('指标健康度：{0}'.format(health_list)) return array(health_list).tolist() # 层次权重 def hierarchy_weight(args): data_array = array(args['data']) # 特征值计算 lamda = np.linalg.eig(data_array) # for i in range(len(lamda[0])): # print('特征值：{0}\n对应的特征向量：\n{1}\n'.format(lamda[0][i], np.transpose([lamda[1][:, i]]))) index = np.argmax(lamda[0]) lamda_max = np.real(lamda[0][index]) vector = lamda[1][:, index] # 特征向量 vector_final = np.transpose((np.real(vector))) # 归一化 one_final = vector_final / np.sum(vector_final) print('层次权重：{0}'.format(one_final.tolist())) print('最大特征值为：{0}\n对应的特征向量：\n{1}\n特征归一化权重值：{2}'.format(lamda_max, vector_final, one_final)) return around(one_final, 4).tolist() # 熵权化 # 定义熵值法函数 def entropy_weight(args): data_array = array(args['data']) print("data_array: ", data_array) '''熵值法计算变量的权重''' # 行列转换 x = data_array.T ''' 如果数据实际不为零，则赋予最小值 if x==0: x=0.00001 else: pass ''' # 求k rows = x.shape[0] # 行 cols = x.shape[1] # 列 if rows == cols == 1: return [1] k = 1.0 / math.log(rows) print("x: ", x) # 矩阵计算-- # 信息熵 x = array(x) lnf = [[None] * cols for i in range(rows)] lnf = array(lnf) for i in range(0, rows): for j in range(0, cols): if x[i][j] == None or (x[j] == None).any(): lnfij = 0.0 else: print("x[i][j]: ", x[i][j]) print("x[i]: ", x[i]) # if (x[j] == None).any(): # lnfij = 0.0 if (x[j] < 0).any() or x[i][j] <= 0: lnfij = 0.0 else: p = x[i][j] / x.sum(axis=0)[j] lnfij = math.log(p) * p print("lnfij: ", lnfij) lnf[i][j] = lnfij E = lnf print("lnf: ", lnf) # 将nan空值转换为0 ej = -k * (E.sum(axis=0)) # 计算每种指标的信息熵 d = (1 - ej) / np.sum(1 - ej) # 计算冗余度 # d = 1 - E.sum(axis=0) print("d: ", d) # 计算各指标的权重 w = [[None] * 1 for i in range(cols)] for j in range(0, cols): wj = d[j] / sum(d) w[j] = round(wj, 4) # 计算各样本的综合得分,用最原始的数据 print('熵权法权重：{0}'.format(w)) return w # 组合常权重 def combination_weight(args): data_array = array(args['data']) # 行列转换 x = data_array.T # 层次权重 hierarchyData = x[0] # 熵权重 entropyData = x[1] # hierarchyData = array(args['hierarchyData']) # entropyData = array(args['entropyData']) # 权重排序 weight_sort = hierarchyData[np.argsort(hierarchyData)] print("weight_sort:", weight_sort) n = np.size(weight_sort) p = 0 for i in range(n): p += (i + 1) * weight_sort[i] print("p:", p) # 线性系数 a = 1 if n != 1: a = round(2 / (n - 1) * p - (n + 1) / (n - 1), 4) print('线性系数：{0}'.format(a)) combination_data = hierarchyData * a + entropyData * (1 - a) print('组合常权重：{0}'.format(combination_data)) return around(combination_data, 4).tolist() # 变权权重(权重，均衡系数) def variable_weight(args): data_array = array(args['data']) # 行列转换 x = data_array.T # 权重 weight = x[0] # 均衡系数 equalization = x[1] # weight = array(args['weight']) # equalization = array(args['equalization']) n = np.size(weight) w_weight = [] for i in range(n): p = 0 for j in range(n): p += equalization[j] * weight[j] w_weight.append(around(p, 4)) print('变权商：{0}'.format(w_weight)) # 变权权重 x = [] for i in range(n): x.append(around(equalization[i] * weight[i] / w_weight[i], 4)) print('变权权重：{0}'.format(x)) # print(x.sum) return x # [0.1928, 0.4692, 0.2657, 0.0723] # 指标均衡系数 def indicator_equilibrium(args): data_array = array(args['data_array']) print(data_array) k = array(args['k']) rows = data_array.shape[0] coefficient_list = [] for i in range(0, rows): # 指标数据（最小阈值，最大阈值，标准值，测量值,报警值） coefficient = equilibrium_coefficient(data_array[i][0], data_array[i][1], data_array[i][2], data_array[i][3], k) # print('指标测量数据：{0}----{1}'.format(data_array[i], health)) coefficient_list.append(round(coefficient, 4)) print('指标均衡系数向量：{0}'.format(coefficient_list)) return coefficient_list # 均衡系数(最小值、最大值、惩戒阈值、测量值、系数) def equilibrium_coefficient(min_data, max_data, threshold, data, k): if (min_data == None or max_data == None or threshold == None or data == None): return 1 else: return round(math.exp(abs((data - threshold) / (max_data - min_data)) * k), 4) # 健康度计算 def health_calculation(weight, health_data): n = np.size(weight) p = 0 for i in range(n): p += weight[i] * health_data[i] print('健康度：{0}'.format(p)) return round(p, 4) # 白化权函数 def gray_cloud_cluster(args): health_data = array(args['data']) weight = array(args['weight']) n = np.size(health_data) # 聚类系数向量 health_cluster = [] # 白化权值 whitening_weight = [] for i in range(n): x = health_data[i] x_health = [] if 0 <= x <= 0.3: x_health.append(math.exp(pow(x, 2) / (2 * pow(0.1, 2)))) else: x_health.append(0) if 0.1 <= x <= 0.5: x_health.append(math.exp(pow((x - 0.3), 2) / (2 * pow(0.067, 2)))) else: x_health.append(0) if 0.3 <= x <= 0.7: x_health.append(math.exp(pow((x - 0.5), 2) /

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