def main(test=0, JSONFileName='JSON_example.json',
CMSDFileName='CMSD_ParallelStations.xml',
DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
jsonFile=None, cmsdFile=None):
if not file_path:
cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
cursors=cnxn.getCursors()
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1=[]
MILL2=[]
for j in range(a.rowcount):
#get the next line
ind1=a.fetchone()
if ind1.stat_code == 'MILL1':
procTime=[]
procTime.insert(0,ind1.TIMEIN)
procTime.insert(1,ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime=[]
procTime.insert(0,ind1.TIMEIN)
procTime.insert(1,ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
transform = Transformations()
procTime_MILL1=[]
for elem in MILL1:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
dt=transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2=[]
for elem in MILL2:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
dt=transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1=[]
MTTF_MILL2=[]
for j in range(b.rowcount):
#get the next line
ind2=b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1=[]
MTTR_MILL2=[]
for j in range(c.rowcount):
#get the next line
ind3=c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
if not cmsdFile:
datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), CMSDFileName)) #It defines the name or the directory of the XML file that is manually written the CMSD information model
tree = et.parse(datafile) #This file will be parsed using the XML.ETREE Python library
exportCMSD=CMSDOutput()
stationId1='M1'
stationId2='M2'
procTime1=exportCMSD.ProcessingTimes(tree, stationId1, distProcTime_MILL1)
procTime2=exportCMSD.ProcessingTimes(procTime1, stationId2, distProcTime_MILL2)
TTF1=exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
TTR1=exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
TTF2=exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
TTR2=exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
TTR2.write('CMSD_ParallelStations_Output.xml',encoding="utf8") #It writes the element tree to a specified file, using the 'utf8' output encoding
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
if not jsonFile:
jsonFile = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), JSONFileName),'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON=JSONOutput()
stationId1='M1'
stationId2='M2'
data1=exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
data2=exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
data3=exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
data4=exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
data5=exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
data6=exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
# if we run from test return the data6
if test:
return data6
jsonFile = open('JSON_ParallelStations_Output.json',"w") #It opens the JSON file
jsonFile.write(json.dumps(data6, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
#=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
export=ExcelOutput()
export.PrintStatisticalMeasures(procTime_MILL1,'procTimeMILL1_StatResults.xls')
export.PrintStatisticalMeasures(procTime_MILL2,'procTimeMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL1,'MTTFMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL2,'MTTFMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL1,'MTTRMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL2,'MTTRMILL2_StatResults.xls')
export.PrintDistributionFit(procTime_MILL1,'procTimeMILL1_DistFitResults.xls')
export.PrintDistributionFit(procTime_MILL2,'procTimeMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL1,'MTTFMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL2,'MTTFMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL1,'MTTRMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL2,'MTTRMILL2_DistFitResults.xls')
def main(test=0,
simul8XMLFileName='ParallelStations.xml',
DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
simul8XMLFile=None):
if not file_path:
cnxn = ConnectionData(seekName='ServerData',
file_path=DBFilePath,
implicitExt='txt',
number_of_cursors=3)
cursors = cnxn.getCursors()
#Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1 = [] #Initialization of MILL1 list
MILL2 = [] #Initialization of MILL2 list
for j in range(a.rowcount):
#get the next line
ind1 = a.fetchone()
if ind1.stat_code == 'MILL1':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
#The BasicTransformations object is called to conduct some data transformations
transform = Transformations()
procTime_MILL1 = []
for elem in MILL1:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2 = []
for elem in MILL2:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
#Database queries used again to extract the MTTF and MTTR data points
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1 = [
] #Initialization of the list that will contain the MTTF data points for MILL1
MTTF_MILL2 = [
] #Initialization of the list that will contain the MTTF data points for MILL2
for j in range(b.rowcount):
#get the next line
ind2 = b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1 = [
] #Initialization of the list that will contain the MTTR data points for MILL1
MTTR_MILL2 = [
] #Initialization of the list that will contain the MTTR data points for MILL1
for j in range(c.rowcount):
#get the next line
ind3 = c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
#The Distributions object is called to fit statistical distributions to the in scope data
dist_proctime = Distributions()
distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2)
#======================= Output preparation: output the updated values in the XML file of this example ================================#
if not simul8XMLFile:
datafile = (os.path.join(os.path.dirname(os.path.realpath(__file__)),
simul8XMLFileName)
) #It defines the name or the directory of the XML file
tree = et.parse(datafile)
else:
datafile = simul8XMLFile
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
#Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
procTimes1 = simul8.ProcTimes(tree, 'MILL1', distProcTime_MILL1)
procTimes2 = simul8.ProcTimes(procTimes1, 'MILL2', distProcTime_MILL2)
#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
MTTF1 = simul8.MTBF(procTimes2, 'MILL1', distMTTF_MILL1)
MTTR1 = simul8.MTTR(MTTF1, 'MILL1', distMTTR_MILL1)
MTTF2 = simul8.MTBF(MTTR1, 'MILL2', distMTTF_MILL2)
MTTR2 = simul8.MTTR(MTTF2, 'MILL2', distMTTR_MILL2)
#Output the XML file with the processed data
output = MTTR2.write('KEtool_ParallelStations.xml')
if test:
output = et.parse('KEtool_ParallelStations.xml')
return output
def main(test=0, simul8XMLFileName='ParallelStations.xml',
DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
file_path=None, simul8XMLFile=None):
if not file_path:
cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
cursors=cnxn.getCursors()
#Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1=[] #Initialization of MILL1 list
MILL2=[] #Initialization of MILL2 list
for j in range(a.rowcount):
#get the next line
ind1=a.fetchone()
if ind1.stat_code == 'MILL1':
procTime=[]
procTime.insert(0,ind1.TIMEIN)
procTime.insert(1,ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime=[]
procTime.insert(0,ind1.TIMEIN)
procTime.insert(1,ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
#The BasicTransformations object is called to conduct some data transformations
transform = Transformations()
procTime_MILL1=[]
for elem in MILL1:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
dt=transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2=[]
for elem in MILL2:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
dt=transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
#Database queries used again to extract the MTTF and MTTR data points
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1=[] #Initialization of the list that will contain the MTTF data points for MILL1
MTTF_MILL2=[] #Initialization of the list that will contain the MTTF data points for MILL2
for j in range(b.rowcount):
#get the next line
ind2=b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1=[] #Initialization of the list that will contain the MTTR data points for MILL1
MTTR_MILL2=[] #Initialization of the list that will contain the MTTR data points for MILL1
for j in range(c.rowcount):
#get the next line
ind3=c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
#The Distributions object is called to fit statistical distributions to the in scope data
dist_proctime = Distributions()
distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2)
#======================= Output preparation: output the updated values in the XML file of this example ================================#
if not simul8XMLFile:
datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), simul8XMLFileName)) #It defines the name or the directory of the XML file
tree = et.parse(datafile)
else:
datafile=simul8XMLFile
tree = et.parse(datafile)
simul8 = Simul8Output() #Call the Simul8Output object
#Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
procTimes1 = simul8.ProcTimes(tree,'MILL1',distProcTime_MILL1)
procTimes2 = simul8.ProcTimes(procTimes1,'MILL2',distProcTime_MILL2)
#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
MTTF1 = simul8.MTBF(procTimes2,'MILL1',distMTTF_MILL1)
MTTR1 = simul8.MTTR(MTTF1,'MILL1',distMTTR_MILL1)
MTTF2 = simul8.MTBF(MTTR1,'MILL2',distMTTF_MILL2)
MTTR2 = simul8.MTTR(MTTF2,'MILL2',distMTTR_MILL2)
#Output the XML file with the processed data
output= MTTR2.write('KEtool_ParallelStations.xml')
if test:
output=et.parse('KEtool_ParallelStations.xml')
return output
def main(test=0,
JSONFileName='JSON_example.json',
CMSDFileName='CMSD_ParallelStations.xml',
DBFilePath='C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
jsonFile=None,
cmsdFile=None):
if not file_path:
cnxn = ConnectionData(seekName='ServerData',
file_path=DBFilePath,
implicitExt='txt',
number_of_cursors=3)
cursors = cnxn.getCursors()
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1 = []
MILL2 = []
for j in range(a.rowcount):
#get the next line
ind1 = a.fetchone()
if ind1.stat_code == 'MILL1':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL1.append(procTime)
elif ind1.stat_code == 'MILL2':
procTime = []
procTime.insert(0, ind1.TIMEIN)
procTime.insert(1, ind1.TIMEOUT)
MILL2.append(procTime)
else:
continue
transform = Transformations()
procTime_MILL1 = []
for elem in MILL1:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2 = []
for elem in MILL2:
t1 = []
t2 = []
t1.append(((elem[0].hour) * 60) * 60 + (elem[0].minute) * 60 +
elem[0].second)
t2.append(((elem[1].hour) * 60) * 60 + (elem[1].minute) * 60 +
elem[1].second)
dt = transform.subtraction(t2, t1)
procTime_MILL2.append(dt[0])
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1 = []
MTTF_MILL2 = []
for j in range(b.rowcount):
#get the next line
ind2 = b.fetchone()
if ind2.stat_code == 'MILL1':
MTTF_MILL1.append(ind2.MTTF_hour)
elif ind2.stat_code == 'MILL2':
MTTF_MILL2.append(ind2.MTTF_hour)
else:
continue
MTTR_MILL1 = []
MTTR_MILL2 = []
for j in range(c.rowcount):
#get the next line
ind3 = c.fetchone()
if ind3.stat_code == 'MILL1':
MTTR_MILL1.append(ind3.MTTR_hour)
elif ind3.stat_code == 'MILL2':
MTTR_MILL2.append(ind3.MTTR_hour)
else:
continue
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
if not cmsdFile:
datafile = (
os.path.join(os.path.dirname(os.path.realpath(__file__)),
CMSDFileName)
) #It defines the name or the directory of the XML file that is manually written the CMSD information model
tree = et.parse(
datafile
) #This file will be parsed using the XML.ETREE Python library
exportCMSD = CMSDOutput()
stationId1 = 'M1'
stationId2 = 'M2'
procTime1 = exportCMSD.ProcessingTimes(tree, stationId1,
distProcTime_MILL1)
procTime2 = exportCMSD.ProcessingTimes(procTime1, stationId2,
distProcTime_MILL2)
TTF1 = exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
TTR1 = exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
TTF2 = exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
TTR2 = exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
TTR2.write(
'CMSD_ParallelStations_Output.xml', encoding="utf8"
) #It writes the element tree to a specified file, using the 'utf8' output encoding
#======================= Output preparation: output the updated values in the JSON file of this example ================================#
if not jsonFile:
jsonFile = open(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
JSONFileName), 'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON = JSONOutput()
stationId1 = 'M1'
stationId2 = 'M2'
data1 = exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
data2 = exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
data3 = exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
data4 = exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
data5 = exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
data6 = exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
# if we run from test return the data6
if test:
return data6
jsonFile = open('JSON_ParallelStations_Output.json',
"w") #It opens the JSON file
jsonFile.write(json.dumps(
data6, indent=True)) #It writes the updated data to the JSON file
jsonFile.close() #It closes the file
#=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
export = ExcelOutput()
export.PrintStatisticalMeasures(procTime_MILL1,
'procTimeMILL1_StatResults.xls')
export.PrintStatisticalMeasures(procTime_MILL2,
'procTimeMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL1, 'MTTFMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL2, 'MTTFMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL1, 'MTTRMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL2, 'MTTRMILL2_StatResults.xls')
export.PrintDistributionFit(procTime_MILL1,
'procTimeMILL1_DistFitResults.xls')
export.PrintDistributionFit(procTime_MILL2,
'procTimeMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL1, 'MTTFMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL2, 'MTTFMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL1, 'MTTRMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL2, 'MTTRMILL2_DistFitResults.xls')
