2022年第二届长三角高校数学建模竞赛B题经验、论文、代码展示

2022年第二届长三角高校数学建模竞赛B题经验、论文、代码展示

1、题目要求
其中数据
附件一数据（截图部分）：
附件二数据（部分截图）：
在这里插入代码片

问题一到问题四的思路：
针对问题一，对附件 1 中的 5 个表单的四个传感器数据进行分析，提取相关特征。
研究发现 VMD 方法在可以避免模态混叠问题。VMD 的低频分量，更容易表达出振动信号故障波动的大趋势。首先，对每个状态，以每 1000 条记录构造样本数据。通过绘制 5个状态下的样本时域图，观察波形的整体情况。其次，对样本数据进行 VMD 变分模态提取四个 IMF 分量。从振动信号的时域特征、频域特征和能量特征三个方面出发，对每个IMF 分量提取构建了均值、峰值、中心频率等 24 个特征，并构建对应特征向量，建立对应的数学公式和计算过程。最后，根据优化组合，得到频域+能量所构建的特征向量可有效区分正常状态和异常状态。差异结果见表 2、3。
针对问题二，要求检测齿轮箱是否故障。首先，在问题一所求 IMF 模态分量的基础上，使用 SVD 降噪重构，对每个 IMF 变量，提取时域、频域、能量域的特征，优化组合，选取频域+能量域构建特征向量。其次，通过构建基于 VMD 的齿轮箱故障检测模型对各个状态的振动信号进行判断。构造样本数据和二分类的标签，以 8:2 划分训练集和测试集，采用 SVM 作为分类器，以准确率为评价指标，进行故障检测。最后得出该模型对振动信号故障检测准确率为 100%。
针对问题三，要求判断齿轮箱具体故障类型。具体故障类型有正常、故障 1、故障2、故障 3、故障 4 共 5 种类型。构造样本数据和五分类的标签，以 8:2 划分训练集和测试集，SVM 作为分类器，以准确率为评价指标，进行故障判断。最后得出该模型对振动信号故障判断准确率为 96.7%。
针对问题四，结合问题二、问题三的模型对测试数据进行检测和判断分析。考虑到
其他故障存在的可能性，通过设置其他类阈值来对检测和判断模型进行修正。当检测和
判断模型对所预测类型的最大概率不大于其他类阈值时，判断为其他故障。最终的测试
数据的诊断结果表如表 4 所示。

问题一代码
`import numpy as np
import matplotlib.pyplot as plt
import xlrd as xd
from vmdpy import VMD
from scipy.fftpack import fft
def xls2npy():
'''转换数据格式
:return:
'''for i in [0,1]:
if i == 0:
curTablePath = "train" else:
curTablePath = "test" # 加载数据
tables = xd.open
_workbook("../data/xls/"+curTablePath+".xls")
sheetNames = tables.sheet
_names()
dict = {}
for i in range(len(sheetNames)):
curSheetName = sheetNames[i]
curSheet = tables.sheet
_by_name(curSheetName)
ncols = curSheet.ncols
# 按列加载每一个 sheet 的每列数据
curSheetData = []
# 去除第一列，即 No 列
for j in range(1, ncols):
curCol = curSheet.col
_values(j)
# 去除第一行
curCol = curCol[1:-1]
curSheetData.append(curCol)
# 对每列进行最大最小归一化，以去除数值过大的影响
curSheetData = np.vstack([item for item in curSheetData])
# 转为（29339，4）
curSheetData = curSheetData.transpose()
dict[curSheetName] = curSheetData
np.save("../data/npy/"+curTablePath+"/dict.npy",dict)
np.save("../data/npy/"+curTablePath+"/sheetNames.npy",sheetNames)
print("_")
def showSensorFeatures():
'''对训练集数据进行 VMD、SVD 后特征提取
:return:
''' basePath = "../data/npy/" dict = np.load(basePath + "train/dict.npy", allow
_pickle=True).item()
# 所有状态的传感器 1
gearBoxs = ['gearbox00', 'gearbox10', 'gearbox20', 'gearbox30', 'gearbox40']
for i in range(len(gearBoxs)):
curGearBoxName = gearBoxs[i]
# 正常状态传感器 1
# 每一千条计算一个样本特征
# 记录当前状态下的四个传感器的特征向量
samplesFeaturesChain = []
for j in range(4):
curGearBoxData = dict[curGearBoxName][:, j]
size = curGearBoxData.shape[0]
curNum = 0
samplesFeatures = []
curLeft = size
data1 = curGearBoxData[curNum:curNum + 1000]
curNum += 1000
curLeft -= 1000
u, u
_hat, omega = vmd(data1)
# 对每个分量进行 svd 去噪
for i in range(len(u)):
curIMF = u[i]
# 对每个分量进行去噪
curIMF = svd(curIMF)
u[i] = curIMF
# 得到每个模态特征向量
curUIMFFeas = calIndicator(u)
# 将当前传感器前 1000 条数据作为一个记录，画出中心模态
dataPlot(data1,4,u)
def vmd(data1):
'''计算当前传感器数据 data1 的四个本征模态分量
:param data1: 传感器数据
:return: 模态分量 u
''' alpha = 7000 # moderate bandwidth constraint
tau = 0. # noise-tolerance (no strict fidelity enforcement)
K = 4 # 3 modes
DC = 0 # no DC part imposed
init = 1 # initialize omegas uniformly
tol = 1e-7
16
# u 是分量
u, u
_hat, omega = VMD(data1, alpha, tau, K, DC, init, tol)
return u, u
_hat, omega
def svd(u
_IMFi):
''' 对某个状态的某个 IMF 分量 u 进行奇异值去噪
:param u
_IMFi:
:return:
''' # series = np.load('../data/npy/train/box00/u.npy')[0,:1000]
series = u
_IMFi
# step1 嵌入
windowLen = 20 # 嵌入窗口长度
seriesLen = len(series) # 序列长度
K = seriesLen - windowLen + 1
X = np.zeros((windowLen, K))
for i in range(K):
X[:, i] = series[i:i + windowLen]
# step2: svd 分解， U 和 sigma 已经按升序排序
U, sigma, VT = np.linalg.svd(X, full
_matrices=False)
for i in range(VT.shape[0]):
VT[i, :] *= sigma[i]
A = VT
# 重组
rec = np.zeros((windowLen, seriesLen))
for i in range(windowLen):
for j in range(windowLen - 1):
for m in range(j + 1):
rec[i, j] += A[i, j - m] * U[m, i]
rec[i, j] /= (j + 1)
for j in range(windowLen - 1, seriesLen - windowLen + 1):
for m in range(windowLen):
rec[i, j] += A[i, j - m] * U[m, i]
rec[i, j] /= windowLen
for j in range(seriesLen - windowLen + 1, seriesLen):
for m in range(j - seriesLen + windowLen, windowLen):
rec[i, j] += A[i, j - m] * U[m, i]
rec[i, j] /= (seriesLen - j)
rrr = np.sum(rec[:2,:], axis=0) # 选择重构的部分，这里选了前三个
return rrr
if
__name
__ == '__main
__':
# 将 xls 文件转化为 npy 文件
xls2npy()
# 展示特征向量
showSensorFeatures()`

问题二代码：
import numpy as np
from sklearn.svm import SVC
from sklearn.model
_selection import train
_test
_split
import scipy.stats
def getSensorFeature():
'''构造传感器的特征向量
:return:
''' basePath = "../data/npy/" # 训练数据
dict = np.load(basePath + "train/dict.npy", allow
_pickle=True).item()
# 测试数据
# dict = np.load(basePath + "test/dict.npy", allow
_pickle=True).item()
# 所有状态的传感器 1
gearBoxs = ['gearbox00','gearbox10','gearbox20','gearbox30','gearbox40']
testBoxs = ['test1','test2','test3','test4',
'test5','test6','test7','test8',
'test9','test10','test11','test12']
samplesFeaturesDict = {}
for i in range(len(gearBoxs)):
curGearBoxName = gearBoxs[i]
# 正常状态传感器 1
# 每一千条计算一个样本特征
curGearBoxData = dict[curGearBoxName]
# 记录当前状态下的四个传感器的特征向量
samplesFeaturesChain = []
for j in range(4):
curGearBoxData = dict[curGearBoxName][:,j]
size = curGearBoxData.shape[0]
curNum = 0
samplesFeatures = []
curLeft = size
while(curLeft>0):
# 得到当前 1000 条数据
steps = 1000
if curLeft<=1000:
steps = curLeft
data1 = curGearBoxData[curNum:curNum+steps]
curNum+=1000
curLeft-=1000
u, u
_hat, omega = vmd(data1)
# 对每个分量进行 svd 去噪
# 若需要原始信号的频谱图，则注释下面的 for 循环
for i in range(len(u)):
curIMF = u[i]
# 对每个分量进行去噪
curIMF = svd(curIMF)
u[i]=curIMF
# 得到每个模态特征向量
curUIMFFeas = calIndicator(u)
samplesFeatures.append(curUIMFFeas)
# (30,52)
samplesFeatures =
np.reshape(samplesFeatures,(len(samplesFeatures),samplesFeatures[0].shape[0]))
samplesFeaturesChain.append(samplesFeatures)
print(".")
# 需要得到 30,52*4 的数据
samplesFeaturesChain = np.hstack([item for item in samplesFeaturesChain])
samplesFeaturesDict[curGearBoxName] = samplesFeaturesChain
# 当需要训练集数据时，消注释这条
np.save('../data/npy/train/sampelsFeaturesDict.npy', samplesFeaturesDict)
# 当需要测试集数据时，消注释这条
# np.save('../data/npy/test/sampelsFeaturesDict.npy', samplesFeaturesDict)
# 特征提取类
class Fea
_Extra():
def
__init
__(self, Signal, Fs = 25600):
self.signal = Signal
self.Fs = Fs
def Time
_fea(self):
""" 提取时域特征 11 类
"""
signal
_ = self.signal
N = len(signal
_)
y = signal
_
# 1
_均值（平均幅值）
t
_mean
_1 = np.mean(y)
# 3
_方根幅值
t
_fgf
_3 = ((np.mean(np.sqrt(np.abs(y)))))**2
# 4
_RMS 均方根
t
_rms
_4 = np.sqrt((np.mean(y**2)))
# 5
_峰峰值
t
_pp_5 = 0.5*(np.max(y)-np.min(y))
# 6
_偏度 skewness
t
_skew
_6 = scipy.stats.skew(y)
# 7
_峭度 Kurtosis
t
_kur
_7 = scipy.stats.kurtosis(y)
# 8
_峰值因子 Crest Factor
t
_cres
_8 = np.max(np.abs(y))/t
_rms
_4
# 9
_裕度因子 Clearance Factor
t
_clear
_9 = np.max(np.abs(y))/t
_fgf
_3
# 10
_波形因子 Shape fator
t
_shape
_10 = (N * t
_rms
_4)/(np.sum(np.abs(y)))
# 11
_脉冲指数 Impulse Fator
t
_imp_11 = ( np.max(np.abs(y)))/(np.mean(np.abs(y)))
t
_max
_12 = np.max(y)
t
_fea = np.array([t
_mean
_1, t
_rms
_4, t
_pp_5, t
_skew
_6, t
_kur
_7, t
_cres
_8, t
_clear
_9, t
_shape
_10, t
_imp_11,t
_max
_12])
return t
_fea
def Fre
_fea(self):
""" 提取频域特征 13 类
:param signal
_:
:return:
"""
signal
_ = self.signal
L = len(signal
_)
PL = abs(np.fft.fft(signal
_ / L))[: int(L / 2)]
PL[0] = 0
f = np.fft.fftfreq(L, 1 / self.Fs)[: int(L / 2)]
x = f
y = PL
K = len(y)
f
_12 = np.mean(y)
f
_13 = np.var(y)
f
_14 = (np.sum((y - f
_12)**3))/(K * ((np.sqrt(f
_13))**3))
f
_15 = (np.sum((y - f
_12)**4))/(K * ((f
_13)**2))
f
_16 = (np.sum(x * y))/(np.sum(y))
f
_17 = np.sqrt((np.mean(((x- f
_16)**2)*(y))))
f
_18 = np.sqrt((np.sum((x**2)*y))/(np.sum(y)))
f
_19 = np.sqrt((np.sum((x**4)*y))/(np.sum((x**2)*y)))
f
_20 = (np.sum((x**2)*y))/(np.sqrt((np.sum(y))*(np.sum((x**4)*y))))
f
_21 = f
_17/f
_16
f
_22 = (np.sum(((x - f
_16)**3)*y))/(K * (f
_17**3))
f
_23 = (np.sum(((x - f
_16)**4)*y))/(K * (f
_17**4))
f
_fea = np.array([f
_12, f
_13, f
_14, f
_15, f
_16, f
_17, f
_18, f
_19, f
_20, f
_21, f
_22, f
_23])
return f
_fea
def calIndicator(u):
''' :param u: 某个状态下模态分量的集合
:return: 某个状态对应模态分量的特征变量
''' Elist = []
timeFeas = []
freqFeas = []
# 对每一个分量机 IMFi 计算时域、频域、能量域的特征值
for i in range(u.shape[0]):
curU = u[i, :]
# 计算能量值
E = 0
for i in curU:
E += (i * i)
Elist.append(E)
# 计算时域值
time
_fea = Fea
_Extra(curU).Time
_fea()
# 计算频域值
freq_fea = Fea
_Extra(curU).Fre
_fea()
timeFeas.append(time
_fea)
freqFeas.append(freq_fea)
totalE = np.sum(Elist)
Elist = Elist / totalE
# 时域特征、频域特征、能量值
IMFFeas = []
for i in range(u.shape[0]):
curIMFFeatures = []
curTimeFea = timeFeas[i]
curFreqFea = freqFeas[i]
curEnergyMark = Elist[i]
# curIMFFeatures.append(curTimeFea)
# 目前实现频域+能量的特征
curIMFFeatures.append(curFreqFea)
curIMFFeatures.append(curEnergyMark)
curIMFFeatures = np.hstack([item for item in curIMFFeatures])
IMFFeas.append(curIMFFeatures)
IMFFeas = np.hstack([item for item in IMFFeas])
return IMFFeas
def structDataSet(sampleDict):
'''根据字典，重构数据集，将其转化为（N,208)的形式
:param sampleDict:
:return:
''' dataSet = []
for gearBox in sampleDict:
curData = sampleDict[gearBox]
dataSet.append(curData)
dataSet = np.reshape(dataSet,(len(dataSet)*dataSet[0].shape[0],dataSet[0].shape[1]))
return dataSet
def normalization(trainData,testData=None):
'''对训练集和测试集进行归一化
:param trainData: 训练集
:param testData: 测试集
:return:
''' normalization
_part1(trainData)
maxList = np.load('../../data/npy/train/maxList.npy')
minList = np.load('../../data/npy/train/minList.npy')
for i in range(trainData.shape[0]):
curRowData = trainData[i]
curRowData = (curRowData - minList) / (maxList-minList)
trainData[i] = curRowData
if testData is not None:
for i in range(testData.shape[0]):
curRowData = testData[i]
curRowData = (curRowData - minList) / (maxList - minList)
testData[i] = curRowData
if testData is None:
return trainData,"" else:
return trainData,testData
def findException(dataSet,gearBoxs):
'''建立故障检测模型
:param dataSet:
:param gearBoxs:
:return:
''' labels = []
# 设置数据标签
for i in range(len(gearBoxs)):
if i not in [0]:
curLabel = np.repeat(1, 30)
else:
curLabel = np.repeat(i, 30)
labels.append(curLabel)
# 将标签合并为一维数据
labels = np.hstack([item for item in labels])
x
_train, x
_test, y_train, y_test = train
_test
_split(dataSet, labels, random
_state=9, train
_size=0.8, stratify=labels)
clf = SVC(kernel='linear', probability=True)
clf.fit(x
_train, y_train)
# 计算准确率
score = clf.score(x
_test,y_test)
print(score)
if
__name
__ == '__main
__':
# 获得传感器数据的特征向量
getSensorFeature()
sampleDict = np.load('../../data/npy/train/sampelsFeaturesDict.npy',allow
_pickle=True).item()
# 所有状态的传感器 1
gearBoxs = ['gearbox00', 'gearbox10', 'gearbox20', 'gearbox30', 'gearbox40']
# 将字典数据转化为矩阵
trainDataSet = structDataSet(sampleDict)
# 对训练数据和测试数据进行归一化
trainData,testData = normalization(trainDataSet,testDataSet)
# 建立故障检测模型
findException(trainData,gearBoxs)

问题三、问题四代码
import numpy as np
from sklearn.svm import SVC
from sklearn.model
_selection import train
_test
_split
def judgeException(dataSet,testData,gearBoxs):
''' 建立故障判断模型
:param dataSet:
:param testData:
:param gearBoxs:
:return:
''' labels = []
# 设置数据标签
for i in range(len(gearBoxs)):
curLabel = np.repeat(i, 30)
labels.append(curLabel)
# 将标签合并为一维数据
labels = np.hstack([item for item in labels])
x
_train, x
_test, y_train, y_test = train
_test
_split(dataSet, labels, random
_state=9, train
_size=0.8, stratify=labels)
clf = SVC(kernel='linear', probability=True)
clf.fit(x
_train, y_train)
pred
_pro = clf.predict
_proba(testData)
pred
_label = clf.predict(testData)
for i in range(len(pred
_pro)):
curPro = pred
_pro[i]
curMaxPro = np.max(curPro)
if curMaxPro <= 0.4:
pred
_label[i] = 5
print(pred
_label)
excpName = ['正常', '异常 1', '异常 2', '异常 3', '异常 4', '其他异常', ]
pred
_Names = []
for i in range(len(pred
_label)):
pred
_Names.append(excpName[pred
_label[i]])
print(pred
_Names)
if
__name
__ == '__main
__':
sampleDict = np.load('../../data/npy/train/sampelsFeaturesDict.npy',allow
_pickle=True).item()
# 所有状态的传感器 1
gearBoxs = ['gearbox00', 'gearbox10', 'gearbox20', 'gearbox30', 'gearbox40']
# 将字典数据转化为矩阵
trainDataSet = structDataSet(sampleDict)
testDict = np.load("../../data/npy/test/sampelsFeaturesDict.npy",allow
_pickle=True).item()
# 将字典数据转化为矩阵
testDataSet = structDataSet(testDict)
# 对训练数据和测试数据进行归一化
trainData,testData = normalization(trainDataSet,testDataSet)
# 建立故障判断模型
judgeException(trainData,testData,gearBoxs)