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ttlFunctions.R
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ttlFunctions.R
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library(DBI)
library(plyr)
library(Rserve)
library(dplyr)
library(tidyr)
library(lubridate)
library(RMySQL)
library(RCurl)
library(jsonlite)
library(RMongo)
library(ggplot2)
library(plotly)
source("/home/cal/Documents/methodBLX/locf.R")
### GLOBAL VARIABLE
SQL_usr <<- "root"
SQL_pass <<- "Siger321"
SQL_host <<- "172.16.33.223"
SQL_port <<- 3306
SQL_huandao_usr <<- "root"
SQL_huandao_pass <<- "Siger321"
SQL_huandao_host <<- "172.16.33.223"
SQL_huandao_port <<- 3306
local_path <<- "/home/cal/Documents/methodBLX/cache"
pic_path <<- "/home/gz2"
########################################################################################################################################################################################################
###
### Main Functions
###
########################################################################################################################################################################################################
computeBLX <- function(rulesAddr){
# READ-IN RULES & DATA.FRAME IT
tmp <- fromJSON(rulesAddr)
ruleVal <- data.frame(startTime = tmp$StartTime,
endTime = tmp$EndTime,
companyID = tmp$companyID,
projectID = tmp$projectID,
machineID = tmp$machineID,
spindleID = tmp$spindleID,
sl_name = paste0("sl", tmp$spindleID),
ss_name = paste0("ss", tmp$spindleID),
tn_name = paste0("tn", tmp$spindleID),
programNum = tmp$PN)
# API
urlAll = read.table(paste0(local_path, "/ipAddress"))
URL_touchBLX <- as.character(urlAll$V2[urlAll$V1 == "urlBLX"])
URL_trainBLX <- getDNSIP(touchURL = URL_touchBLX, typeString = "/Api/convertDB/getDBDetails", paramDF = data.frame(companyID = ruleVal$companyID, projectID = ruleVal$projectID))
# PREPARE LIST TO TRANSFER
list2json = list(ver = "1",
companyid = ruleVal$companyID,
projectid = ruleVal$projectID,
data = data.frame(MachineID = ruleVal$machineID, Starttime = ruleVal$startTime, Endtime = ruleVal$endTime))
# LIST TO JSON
json2post <- jsonlite::toJSON(list2json,method = "C", auto_unbox = TRUE)
# JSON TO TRANSFER
recData <- RCurl::postForm(uri = URL_trainBLX,
.opts = list(postfields = json2post, httpheader = c('Content-Type' = 'application/json', Accept = 'application/json')))
# GET DATA
rst <- fromJSON(recData)
output <- rst$data
# RETURN IF NO DATA | CONNECTION FAILED
if (rst[[1]] != 1){return(toJSON(list(ret = rst[[1]]), auto_unbox=TRUE))}
if (is.null(nrow(output))){return(toJSON(list(ret = 33), auto_unbox=TRUE))}
if (nrow(output) == 0){return(toJSON(list(ret = 44), auto_unbox=TRUE))}
# RENAME
output2 <- plyr::rename(output, c('mt'='machine_type', 'opm'='operating_mode', 'pst'='program_state', 'al'='alarm_state',
'pn'='program_number', 'spn' = 'subprogram_number',
'stn'='step_number','cc'='cycle_counter', 't'='time'))
if ((!(ruleVal$sl_name %in% colnames(output2))) || (!(ruleVal$ss_name %in% colnames(output2))) || (!(ruleVal$tn_name %in% colnames(output2)))){return(44)}
names(output2)[names(output2) == ruleVal$sl_name]="spindle_load"
names(output2)[names(output2) == ruleVal$ss_name]="spindle_speed"
names(output2)[names(output2) == ruleVal$tn_name]="tool_number"
# CLEAN DATA
machineType <- ruleVal[['companyID']]
if (!machineType %in% c(36,39)){machineType = "jc"}
dt <- dataClean(output2, machineType = machineType)
# PROGRAM NUMBER
if(!is.na(which(unique(dt$program_number) %in% ruleVal[['programNum']] == 1)[1])){
programNum <- unique(dt$program_number)[which(unique(dt$program_number) %in% ruleVal[['programNum']] == 1)]
dt <- dt %>% filter(program_number == programNum)
}else if(!is.na(which(unique(dt$subprogram_number) %in% ruleVal[['programNum']] == 1)[1])){
programNum <- unique(dt$subprogram_number)[which(unique(dt$subprogram_number) %in% ruleVal[['programNum']] == 1)]
dt <- dt %>% filter(subprogram_number == programNum)
}else{
return(toJSON(list(ret = 46), auto_unbox=TRUE))
}
if (nrow(dt) == 0){return(toJSON(list(ret = 44), auto_unbox=TRUE))}
# REMOVE DUPLICATES
dt[['_Id']] = NULL
dt = dt[!duplicated(dt),]
# TRAIN BLX
coordBLX <- trainBLX(dt = dt, ruleVal = ruleVal)
# SPECIFIC MODIFICATION FOR 92
# if (ruleVal$machineID == 92){
# coordBLX[['step_number']] = seq(nrow(coordBLX))
# }
# ggplot(coordBLX, aes(x=step_number)) +
# geom_step(aes(y=LL), color="blue") +
# geom_step(aes(y=UL), color="red")
# LIST TO JSON
x = list(ret=jsonlite::unbox(1), step = coordBLX$step_number, lower = coordBLX$LL, upper = coordBLX$UL)
tmp = jsonlite::toJSON(x)
return(tmp)
}
plotHuandao <- function(rulesAddr){
# READ-IN RULES & DATA.FRAME IT ------------
tmp <- fromJSON(rulesAddr)
ruleVal <- data.frame(startTime = as.POSIXct(as.numeric(tmp$StartTime), origin="1970-01-01"),
endTime = as.POSIXct(as.numeric(tmp$EndTime), origin="1970-01-01"),
companyID = tmp$companyID, projectID = tmp$projectID, machineID = tmp$machineID, spindleID = tmp$spindleID, sl_name = paste0("sl", tmp$spindleID), toolNum = tmp$toolLocationNum, programNum = tmp$PN)
# ESTABLISH TREND MYSQL CONNECTION
connT <- dbConnect(RMySQL::MySQL(), dbname = "KM", username = SQL_usr, password = SQL_pass, host = SQL_host, port = SQL_port)
on.exit(DBI::dbDisconnect(connT))
# READ KM_Trend RECORDS
hist <- DBI::dbGetQuery(connT, sqlInterpolate(connT,
"select * from KM.KM_Trend where companyID = ?companyID AND projectID = ?projectID AND machineID = ?machineID AND programNum = ?programNum AND spindleID = ?spindleID AND toolNum = ?toolNum AND time >= ?startTime AND time <= ?endTime",
companyID = ruleVal$companyID, projectID = ruleVal$projectID, machineID = ruleVal$machineID, programNum = ruleVal$programNum, spindleID = ruleVal$spindleID, toolNum = ruleVal$toolNum, startTime = as.character(ruleVal$startTime), endTime = as.character(ruleVal$endTime)))
hist <- hist[order(hist$time, decreasing = FALSE),]
# ERROR
if (nrow(hist) == 0){return(toJSON(list(ret=44), auto_unbox=TRUE))}
# ESTABLISH HUANDAO MYSQL CONNECTION
connH <- dbConnect(RMySQL::MySQL(), dbname = "siger", username = SQL_huandao_usr, password = SQL_huandao_pass, host = SQL_huandao_host, port = SQL_huandao_port)
on.exit(DBI::dbDisconnect(connH))
# READ HUANDAO RECORDS
tmp <- DBI::dbGetQuery(connH, sqlInterpolate(connH,
"select * from siger_tool_change_record where projectid = ?projectID AND machine_id = ?machineID AND mainaxis = ?spindleID AND tool_no = ?toolNum AND change_time >= ?startTime AND change_time <= ?endTime",
projectID = ruleVal$projectID, machineID = ruleVal$machineID, spindleID = ruleVal$spindleID, toolNum = ruleVal$toolNum, startTime = as.numeric(tmp$StartTime), endTime = as.numeric(tmp$EndTime)))
huandao <- as.POSIXct(as.numeric(tmp$change_time), origin = "1970-01-01")
# PLOT
path <- plotOneTime(hist = hist, huandao = huandao, ruleVal = ruleVal)
fullpath <- paste0(pic_path, "/", path)
# RST
rst <- list(ret = 1, path = path)
return(toJSON(rst, auto_unbox = TRUE))
}
computeBOXPLOT <- function(rulesAddr){
# READ-IN RULES & DATA.FRAME IT
tmp <- fromJSON(rulesAddr)
ruleVal <- list(startTime = as.POSIXct(as.numeric(tmp$StartTime), origin="1970-01-01"),
endTime = as.POSIXct(as.numeric(tmp$EndTime), origin="1970-01-01"),
companyID = tmp$companyID, projectID = tmp$projectID, machineID = tmp$machineID, spindleID = tmp$spindleID, sl_name = paste0("sl", tmp$spindleID), toolNum = tmp$toolLocationNum, programNum = tmp$PN)
# COMPUTE SUMMARY STATISTICS
rst <- sapply(ruleVal$toolNum, computeStatistics, ruleVal = ruleVal)
if (44 %in% rst){return(toJSON(list(ret=44), auto_unbox=TRUE))}
# LIST TO JSON
tmp = jsonlite::toJSON(list(ret=1, data=t(rst)), auto_unbox = TRUE)
return(tmp)
}
computeSpeed <- function(rulesAddr){
### () READ-IN RULES & DATA.FRAME IT
# READ-IN RULES
tmp <- fromJSON(rulesAddr)
# WRITE RULES TO HARD-DRIVE
ruleWrite <- write.csv(tmp, file=paste0(local_path, "/tmpSSRule.csv"))
# READ HARD-DRIVE RULES
ruleRead <- read.csv(file = paste0(local_path, "/tmpSSRule.csv"))
# DATA.FRAME RULES
tmp <- as.data.frame(ruleRead)
### () UN-PUZZLE RULES
# DELETE NOISE COLUMNS
tmpRule <- as.vector(tmp[,3:ncol(tmp)])
# IF INPUT RULE IS WRONG SIZE
if((ncol(tmpRule) %% 8) != 0){return(toJSON(list(ret=22), auto_unbox=TRUE))}
# DATA.FRAME RULES
rules <- as.data.frame(t(matrix(tmpRule, 8, ncol(tmpRule)/8)))
# RENAME COLUMN NAMES
colnames(rules) <- c("companyID", "projectID", "spindleID", "machineID", "startTime", "endTime", "toolNum", "PN")
# CONVERT TO WORKABLE RULEDATA
ruleData <- list(companyID = as.numeric(unique(rules$companyID)),
projectID = as.numeric(unique(rules$projectID)),
machineID = as.numeric(unique(rules$machineID)),
programNum = as.numeric(unique(rules$PN)),
startTime = as.numeric(unique(rules$startTime)),
endTime = as.numeric(unique(rules$endTime)))
### () GET DATA
# API
urlAll = read.table(paste0(local_path, "/ipAddress"))
URL_touchBLX <- as.character(urlAll$V2[urlAll$V1 == "urlBLX"])
URL_trainBLX <- getDNSIP(touchURL = URL_touchBLX, typeString = "/Api/convertDB/getDBDetails", paramDF = data.frame(companyID = ruleData$companyID, projectID = ruleData$projectID))
# PREPARE LIST TO TRANSFER
list2json = list(ver = "1",
companyid = ruleData$companyID,
projectid = ruleData$projectID,
data = data.frame(MachineID = ruleData$machineID, Starttime = ruleData$startTime, Endtime = ruleData$endTime, Program = ruleData$programNum))
# LIST TO JSON
json2post <- jsonlite::toJSON(list2json, method = "C", auto_unbox = TRUE)
# JSON TO TRANSFER
recData <- RCurl::postForm(uri = URL_trainBLX,
.opts = list(postfields = json2post, httpheader = c('Content-Type' = 'application/json', Accept = 'application/json')))
# GET DATA
rst <- fromJSON(recData)
output <- rst$data
# RETURN IF NO DATA
if (is.null(nrow(output))){return(toJSON(list(ret = 44), auto_unbox=TRUE))}
if (nrow(output) < 5){return(toJSON(list(ret = 44), auto_unbox=TRUE))}
# RENAME
output2 <- plyr::rename(output, c('mt'='machine_type', 'opm'='operating_mode', 'pst'='program_state', 'al'='alarm_state',
'pn'='program_number', 'spn' = 'subprogram_number',
'stn'='step_number','cc'='cycle_counter', 't'='time'))
### () COMPUTATION
# INITIAL EMPTY RESULT
knivesStat = list(ret = 0, data = list())
# COMPUTE BASED ON EACH SPINDLE
for (i in 1:length(unique(rules$spindleID))){
# CURRENT SPINDLE i RULEVAL
tmp <- rules %>% filter(spindleID == unique(rules$spindleID)[[i]])
curr_ruleVal <- data.frame(companyID = as.numeric(unique(tmp$companyID)),
projectID = as.numeric(unique(tmp$projectID)),
machineID = as.numeric(unique(tmp$machineID)),
spindleID = as.numeric(unique(tmp$spindleID)),
programNum = as.numeric(unique(tmp$PN)))
# RENAME
dtTmp <- output2
if(paste0("sl", curr_ruleVal$spindleID) %in% colnames(dtTmp)){
names(dtTmp)[names(dtTmp)==paste0("sl", curr_ruleVal$spindleID)]="spindle_load"
names(dtTmp)[names(dtTmp)==paste0("ss", curr_ruleVal$spindleID)]="spindle_speed"
names(dtTmp)[names(dtTmp)==paste0("tn", curr_ruleVal$spindleID)]="tool_number"
}else{
return(toJSON(list(ret=88), auto_unbox=TRUE))
}
# DATA CLEAN
# GET CYCLE COUNTER
currCycle <- plyr::count(dtTmp$cycle_counter)$x[which(plyr::count(dtTmp$cycle_counter)$freq == max(plyr::count(dtTmp[dtTmp$cycle_counter!=0,]$cycle_counter)$freq))]
dtTmp$cycle_counter[which(dtTmp$cycle_counter == 0)] <- currCycle
# DATA CLEAN
dt <- dtTmp %>%
filter(cycle_counter == currCycle)
# dt <- dataClean(dt) %>%
# filter(dt$cycle_counter == count(dt$cycle_counter)[which(count(dt$cycle_counter)$freq == max(count(dt$cycle_counter)$freq))])
# RETURN IF NO DATA
if (nrow(dt) < 1){return(toJSON(list(ret=47), auto_unbox=TRUE))}
# CHOOSE KNIVES TO ANALYZE
dtTool <- dt %>%
dplyr::group_by(tool_number) %>%
dplyr::filter(length(spindle_load) >= 3)
knives <- sort(as.numeric(unique(dtTool$tool_number)))
# # PROGRAM NUMBER
# if(!is.na(which(unique(dt$program_number) %in% curr_ruleVal[['programNum']] == 1)[1])){
# programNum <- unique(dt$program_number)[which(unique(dt$program_number) %in% curr_ruleVal[['programNum']] == 1)]
# dt <- dt %>% filter(program_number == programNum)
# }else if(!is.na(which(unique(dt$subprogram_number) %in% curr_ruleVal[['programNum']] == 1)[1])){
# programNum <- unique(dt$subprogram_number)[which(unique(dt$subprogram_number) %in% curr_ruleVal[['programNum']] == 1)]
# dt <- dt %>% filter(subprogram_number == programNum)
# }else{
# return(toJSON(list(ret=46), auto_unbox=TRUE))
# }
# SET PATH
path1 = paste0("/OneTime/", Sys.Date(), "_", as.numeric(Sys.time()), "_1.jpeg")
path2 = paste0("/OneTime/", Sys.Date(), "_", as.numeric(Sys.time()), "_2.jpeg")
path3 = paste0("/OneTime/", Sys.Date(), "_", as.numeric(Sys.time()), "_3.jpeg")
fullpath1 = paste0(pic_path, as.character(path1))
fullpath2 = paste0(pic_path, as.character(path2))
fullpath3 = paste0(pic_path, as.character(path3))
# COMPUTATION
knivesStat[['data']][[i]] <- list(path = list(path1 = as.character(path1),
path2 = as.character(path2),
path3 = as.character(path3)),
t(sapply(knives, computeSS, dt=dt, spindleID = curr_ruleVal$spindleID)))
knivesStat[['ret']][[i]] <- 1
# PLOT
dtPlot <- dt[order(dt$time, decreasing = FALSE),]
# MODIFY SEVERAL COLUMNS
dtPlot$time <- ymd_hms(dtPlot$time)
dtPlot$spindle_load <- dtPlot$spindle_load/10000
dtPlot$spindle_speed <- dtPlot$spindle_speed/10000
dtPlot$fre <- dtPlot$fre/10000
# PLOT SPINDLE_LOAD
ggplot(dtPlot, aes(x=as.numeric(time-time[1]), y=spindle_load)) +
geom_line(color = "gray", size=1, alpha=0.7) +
geom_point(aes(color = as.factor(tool_number)), size=5.5, alpha=0.7) +
scale_y_continuous(labels=scales::percent) +
labs(colour = "刀具号") +
xlab("时间(s)") +
ylab("负载") +
theme(legend.title = element_text(size=30),
legend.text = element_text(size=30),
axis.title.x = element_text(size=30),axis.text.x = element_text(size=30),
axis.title.y = element_text(size=30),axis.text.y = element_text(size=30),
panel.background = element_rect(fill="#F4F4F4",color = "#F4F4F4"),
plot.background = element_rect(fill = "#F4F4F4", color = "#F4F4F4"))
ggsave(filename=fullpath1, device = "jpeg", width = 70, height = 20, dpi = 150, limitsize = FALSE, units = "cm")
# PLOT SPINDLE_SPEED
ggplot(dtPlot, aes(x=as.numeric(time-time[1]), y=spindle_speed)) +
geom_line(color = "gray", size=1, alpha=0.7) +
geom_point(aes(color = as.factor(tool_number)), size=5.5, alpha=0.7) +
scale_y_continuous(labels=scales::percent) +
labs(colour = "刀具号") +
xlab("时间(s)") +
ylab("转速") +
theme(legend.title = element_text(size=30),
legend.text = element_text(size=30),
axis.title.x = element_text(size=30),axis.text.x = element_text(size=30),
axis.title.y = element_text(size=30),axis.text.y = element_text(size=30),
panel.background = element_rect(fill="#F4F4F4",color = "#F4F4F4"),
plot.background = element_rect(fill = "#F4F4F4", color = "#F4F4F4"))
ggsave(filename=fullpath2, device = "jpeg", width = 70, height = 20, dpi = 150, limitsize = FALSE, units = "cm")
# PLOT FEED_RATE
ggplot(dtPlot, aes(x=as.numeric(time-time[1]), y=fre)) +
geom_line(color = "gray", size=1, alpha=0.7) +
geom_point(aes(color = as.factor(tool_number)), size=5.5, alpha=0.7) +
# geom_point(aes(y=spindle_speed * coeffi, color=as.factor(tool_number)), size=5.5, alpha=1) +
# geom_line(aes(y=spindle_speed * coeffi), color="grey", size=1, alpha = 0.7) +
scale_y_continuous(labels=scales::percent) +
labs(colour = "刀具号") +
xlab("时间(s)") +
ylab("进给量") +
theme(legend.title = element_text(size=30),
legend.text = element_text(size=30),
axis.title.x = element_text(size=30),axis.text.x = element_text(size=30),
axis.title.y = element_text(size=30),axis.text.y = element_text(size=30),
panel.background = element_rect(fill="#F4F4F4",color = "#F4F4F4"),
plot.background = element_rect(fill = "#F4F4F4", color = "#F4F4F4"))
ggsave(filename=fullpath3, device = "jpeg", width = 70, height = 20, dpi = 150, limitsize = FALSE, units = "cm")
}
### () RETURN
tmp <- toJSON(knivesStat, auto_unbox = TRUE)
return(tmp)
}
getProgramNum <- function(rulesAddr){
### () READ-IN RULES & DATA.FRAME IT
# READ-IN RULES
tmp <- fromJSON(rulesAddr)
# WRITE RULES TO HARD-DRIVE
ruleWrite <- write.csv(tmp, file=paste0(local_path, "/tmpSSRule.csv"))
# READ HARD-DRIVE RULES
ruleRead <- read.csv(file = paste0(local_path, "/tmpSSRule.csv"))
# DATA.FRAME RULES
tmp <- as.data.frame(ruleRead)
### () UN-PUZZLE RULES
# DELETE NOISE COLUMNS
tmpRule <- as.vector(tmp[,3:ncol(tmp)])
# IF INPUT RULE IS WRONG SIZE
# if((ncol(tmpRule) %% 8) != 0){return(toJSON(list(ret=22), auto_unbox=TRUE))}
# DATA.FRAME RULES
# rules <- as.data.frame(t(matrix(tmpRule, 8, ncol(tmpRule)/8)))
rules <- as.data.frame(tmpRule)
# RENAME COLUMN NAMES
colnames(rules) <- c("companyID", "projectID", "machineID", "spindleID", "startTime", "endTime")
# CONVERT TO WORKABLE RULEDATA
ruleData <- list(companyID = as.numeric(unique(rules$companyID)),
projectID = as.numeric(unique(rules$projectID)),
machineID = as.numeric(unique(rules$machineID)),
startTime = as.numeric(unique(rules$startTime)),
endTime = as.numeric(unique(rules$endTime)))
### () GET PROGRAM NUMBER
# ESTABLISH KM MYSQL CONNECTION
connT <- dbConnect(RMySQL::MySQL(), dbname = "KM", username = SQL_usr, password = SQL_pass, host = SQL_host, port = SQL_port)
on.exit(DBI::dbDisconnect(connT))
# GET CORRESEPONDING PROGRAM NUMBER
allProgramNum <- DBI::dbGetQuery(connT, sqlInterpolate(connT,
"select programNum from KM.KM_Trend where companyID = ?companyID AND projectID = ?projectID AND machineID = ?machineID AND time >= ?startTime AND time <= ?endTime",
companyID = ruleData$companyID, projectID = ruleData$projectID, machineID = ruleData$machineID, startTime = as.character(as.POSIXct(ruleData$startTime, origin = "1970-01-01")), endTime = as.character(as.POSIXct(ruleData$endTime, origin = "1970-01-01"))))
# ERROR
if ((nrow(allProgramNum) == 0) | is.na(nrow(allProgramNum)[1])){return(toJSON(list(ret=44), auto_unbox=TRUE))}
# RETURN PROGRAM NUMBER
tmp <- list(ret = 1, data = list(programNum = as.array(unique(allProgramNum[['programNum']]))))
rst <- toJSON(tmp, auto_unbox = TRUE)
return(rst)
}
########################################################################################################################################################################################################
###
### Side Functions
###
########################################################################################################################################################################################################
dataClean <- function(dt, machineType = "jc", missingValTol = 0.1){
### (-) MISSING VALUES
# FIND ALL MISSING VALUES
missingIndex <- which(complete.cases(dt) != 1)
# NO MISSING ROWS
if (is.na(missingIndex[1])){
dt <- dt
# MISSING UNDER TOLERANT LEVEL
}else if((length(missingIndex)/nrow(dt) < missingValTol) && (nrow(dt)>1000)){
dt <- dt[!missingIndex,]
# FILL USING ADJACENT POINTS
}else{
for (i in 1:ncol(dt)){
dt[,i] <- forbak(dt[,i])
}
}
### (-) CYCLE_COUNTER ALWAYS INCREASING CHECK
# DROP CYCLE_COUNTER == 0
dt = dt[which(dt$cycle_counter!=0),]
# DETECT DROPS IN CYCLE_COUNTER
ind <- which((dt[['cycle_counter']] - lag(dt[['cycle_counter']])) < 0)
# GET DROP CYCLE_COUNTER VALUES
increment <- dt[['cycle_counter']][ind - 1] - dt[['cycle_counter']][ind]
# INCREASE FOLLOWING CYCLE_COUNTER
if (!is.na(increment[1])){
for (i in 1:length(increment)){
dt[['cycle_counter']][ind[i]:length(dt[['cycle_counter']])] = dt[['cycle_counter']][ind[i]:length(dt[['cycle_counter']])] + increment[i] + 1
}
}
### (-) FILTERING
if (machineType == "jc"){
dt2 <- dt %>% filter(alarm_state == 0,
program_state == 3,
operating_mode == 1)
}else if (machineType == 36){
dt2 <- dt %>%
filter(program_state == 3,
operating_mode == 1,
abs(spindle_speed - lag(spindle_speed)) < 1) %>%
filter(abs(spindle_speed - lag(spindle_speed)) < 1,
spindle_load > 1,
spindle_speed > 0)
}else if (machineType == 39){
dt2 <- dt %>%
filter(program_state == 3,
operating_mode == 1,
abs(spindle_speed - lag(spindle_speed)) < 1) %>%
filter(abs(spindle_speed - lag(spindle_speed)) < 1,
spindle_load > 1,
spindle_speed > 0) %>%
filter(abs(spindle_speed - lag(spindle_speed)) < 1)
}
return(dt2)
}
getDNSIP <- function(touchURL, typeString, paramDF){
# PREPARE LIST TO TRANSFER
list2json = list(ver = "1",
companyid = paramDF$companyID,
projectid = paramDF$projectID)
# LIST TO JSON
json2post <- jsonlite::toJSON(list2json,method = "C", auto_unbox = TRUE)
# JSON TO TRANSFER
recData <- RCurl::postForm(uri = touchURL,
.opts = list(postfields = json2post, httpheader = c('Content-Type' = 'application/json', Accept = 'application/json')))
# GET URL
tmp <- fromJSON(recData)
ipAddr <- tmp[["data"]][["statusCalcUrl"]]
# GET ALARM URL
newestURL <- paste0(ipAddr, typeString)
return(newestURL)
}
trainBLX <- function(dt, ruleVal, machineType = "jc"){
# SET UP RAW DATASET
dt <- dt %>% select(cycle_counter, step_number, spindle_load, time) %>% as.data.frame()
# TRAIN BLX VALUES BASED ON MACHINE TYPE
#if (machineType == "jc"){
# SETTINGS
trimLevel = 0.1
max_sig <- 0.1; #max_sig <- ruleVal$max_sig_level
min_sig <- 0.1; #min_sig <- ruleVal$min_sig_level
# EXTRACT SUMMARISE TABLE
tmp <- dt %>%
dplyr::group_by(cycle_counter, step_number) %>%
dplyr::summarise(time = first(time), feature = max(spindle_load, trim=trimLevel, na.rm = TRUE)) %>%
arrange(cycle_counter, step_number)
# SET BLX LIMITS
rst <- tmp %>% filter(feature > 0) %>%
dplyr::group_by(step_number) %>%
dplyr::summarise(ll = max(qnorm(0.0013, mean(feature), sd(feature), lower.tail = TRUE),
(1-min_sig)*max(min(feature),2/(1-min_sig))),
ul = min(qnorm(0.0013, mean(feature), sd(feature), lower.tail = FALSE),
(1+max_sig)*max(feature)),
LL_act = min(feature, na.rm=TRUE),
UL_act = max(feature, na.rm=TRUE),
LL = log10(ifelse(LL_act < 1, 1, LL_act)) * (1-min_sig),
UL = log10(ifelse(UL_act < 1, 3, UL_act)) * (1+max_sig),
#LL = log(ifelse(min(feature, na.rm=TRUE) < 1, 1, min(feature, na.rm=TRUE)) * (1 - min_sig)),
#UL = log(ifelse(max(feature, na.rm=TRUE) < 1, 3, max(feature, na.rm=TRUE)) * (1 + max_sig)),
std = ifelse(length(unique(feature)) == 1, 500000, sd(feature)),
pVal = ifelse(((length(unique(feature)) <= 1) | (length(feature) < 3)), NA, shapiro.test(feature)$p.value)) %>%
#na.omit() %>%
arrange(as.numeric(step_number))
rst <- as.data.frame(rst)
rst$LL[1] = log10(1) * (1-min_sig)
rst$UL[1] = log10(3) * (1+max_sig)
#}else if (machineType == "zc"){
#}
# INITIALIZATION
companyID <- ruleVal$companyID
projectID <- ruleVal$projectID
machineID <- ruleVal$machineID
spindleID <- ruleVal$spindleID
programNum <- ruleVal$programNum
# GET VALUES TO INSERT
step_number <- rst$step_number
step_number_manual <- seq_along(rst$step_number)
UL <- rst$UL
LL <- rst$LL
UL_act <- rst$UL_act
LL_act <- rst$LL_act
STD_act <- rst$std
pVal <- rst$pVal
# DATA.FRAME VALUES
values <- data.frame(companyID=companyID, projectID=projectID, machineID=machineID, programNum = programNum, spindleID = spindleID,
step_number=step_number, step_number_manual=step_number_manual, LL=LL, UL=UL, LL_act = LL_act, UL_act = UL_act, STD_act=STD_act, pVal=pVal, time=NA, currCycle=NA, act=NA, tool_number=NA)
# SET UP SQL CONNECTION
connB <- dbConnect(RMySQL::MySQL(), dbname = "KM", username = SQL_usr, password = SQL_pass, host = SQL_host, port = SQL_port)
on.exit(DBI::dbDisconnect(connB))
# WRITE & SEND QUERY
query <- sqlInterpolate(connB,
"delete from KM.KM_BLX where companyID = ?companyID AND projectID = ?projectID AND machineID = ?machineID AND programNum = ?programNum AND spindleID = ?spindleID",
companyID = companyID, projectID = projectID, machineID = machineID, programNum = programNum, spindleID = spindleID)
dbSendQuery(connB, query)
# UPDATE BLX
dbWriteTable(connB, "KM_BLX", values, overwrite = FALSE, append = TRUE, row.names = FALSE)
return(rst)
}
plotOneTime <- function(hist, huandao, ruleVal, ruleDay = 7){
# FORMAT & ORDER BY TIME
hist$time = ymd_hms(hist$time, tz="Asia/Shanghai")
hist = hist[order(hist$time, decreasing=FALSE),]
# SET X AXIS
hist$cycle_counter_manual = seq(nrow(hist))
# SET AXIS STEP
max_cc = max(hist$cycle_counter_manual)
by_interval = ceiling(max_cc/10)
# SCALING
hist$feature = hist$feature/10000
# CONVERT HUANDAO TIME POINTS TO CYCLE POINTS
if (!is.na(huandao[1])){
huandaoLoc = 0
for (i in 1:length(huandao)){
huandaoLoc <- c(huandaoLoc, which(hist$time >= huandao[i])[1])
}
huandaoLoc <- huandaoLoc[2:length(huandaoLoc)]
huandaoLoc <- data.frame(huandao = huandaoLoc[which(!is.na(huandaoLoc))])
}else{
huandaoLoc = data.frame(huandao=1)
}
huandaoTime = hist$time[huandaoLoc$huandao]
# DROP THE FIRST POINT AFTER HUANDAO
hist = hist[-(unique(huandaoLoc$huandao)+1),]
# SET PLOTTING DIRECTORY
path = paste0("/OneTime/", Sys.Date(), "_", as.numeric(Sys.time()), "_", ruleVal$toolNum)
fullpath = paste0(pic_path, as.character(path), ".jpeg")
path2return = paste0(as.character(path), ".jpeg")
# PLOT
if (nrow(hist) > ruleDay * 3){
# CREATE TWO CATEGORIES: 1. POINTS; 2. HUANDAO SEGMENT
hist <- hist %>% mutate(point_subgroup = 0,
huandaoRec = 0)
# SET HUANDAO SEGMENTS
hist$huandaoRec[huandaoLoc$huandao] = 1
hist$huandaoRec = cumsum(hist$huandaoRec)
# SET UPPER & LOWER LIMITS FOR EACH SEGMENT
hist <- hist %>% group_by(huandaoRec) %>%
mutate(LL = max(0, mean(feature[1:min(length(feature), ruleDay)]) - 3*sd(feature[1:min(length(feature), ruleDay)])),
UL = mean(feature[1:min(length(feature), ruleDay)]) + 3*sd(feature[1:min(length(feature), ruleDay)]),
avg = mean(feature))
hist$point_subgroup[which(hist$feature>hist$UL)] = 1
hist$point_subgroup[which(hist$feature<hist$LL)] = 2
# GGPLOT
ggplot(hist, aes(x = time, y = feature, col = as.factor(point_subgroup))) +
geom_line(color="#cccccc",size=0.5,alpha=0.6) +
geom_point(size=4.5, alpha=0.8) +
scale_color_manual(values = c("0" = "gray45", "1"= "red1", "2"="red1")) +
geom_step(aes(y=LL), color="red4", size=1.5, alpha=0.8) +
geom_step(aes(y=UL), color="red4", size=1.5, alpha=0.8) +
geom_step(aes(y=avg), color="green", size=1.5, alpha=1) +
# geom_smooth(aes(group = as.factor(huandaoRec)), se = FALSE, color = "blue", method="lm", size=0.7, alpha=0.7) +
geom_vline(data=as.data.frame(huandaoTime),aes(xintercept = huandaoTime), color="black", size=1.2) +
xlab("时间") + ylab("负载") + ggtitle(paste0("刀具", ruleVal$toolNum, "-负载随时间变化趋势")) +
#theme_bw() +
# scale_x_continuous(breaks = seq(from = 0, to = max_cc, by = by_interval), label = seq(from = 0, to = max_cc, by = by_interval)) +
scale_y_continuous(labels = scales::percent) +
theme(legend.position = "none",
axis.title.x = element_text(size=30),axis.text.x = element_text(size=30),
axis.title.y = element_text(size=30),axis.text.y = element_text(size=30),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(fill="#F4F4F4", colour = "#F4F4F4"),
plot.title = element_text(size=30),
plot.background = element_rect(fill = "#F4F4F4", colour = "#F4F4F4"))
# SAVE PLOT
ggsave(filename = fullpath, device = "jpeg", width = 70, height = 20, dpi = 60, limitsize = FALSE, units = "cm")
}else{
# CREATE TWO CATEGORIES: 1. POINTS; 2. HUANDAO SEGMENT
hist <- hist %>% mutate(point_subgroup = 0,
huandaoRec = 0)
# SET HUANDAO SEGMENTS
hist$huandaoRec[huandaoLoc$huandao] = 1
hist$huandaoRec = cumsum(hist$huandaoRec)
# GGPLOT
ggplot(hist, aes(x = time,y = feature)) +
geom_line(color="black",size=0.5,alpha=0.6) +
#geom_step(aes(y=LL), linetype = "longdash", color="green4", size=0.8, alpha=0.8) +
#geom_step(aes(y=UL), linetype = "longdash", color="red4", size=0.8, alpha=0.8) +
#geom_step(aes(y=avg), color="yellow3", size=0.7, alpha=1) +
geom_point(aes(color=as.factor(point_subgroup)), size=4, alpha=0.9) +
# geom_smooth(aes(group=as.factor(huandaoRec)), se = FALSE, color = "blue", method="lm", size=0.7, alpha=0.7) +
geom_vline(data=as.data.frame(huandaoTime),aes(xintercept = huandaoTime), color="black", size=1.2) +
xlab("时间") + ylab("负载") + ggtitle(paste0("刀具", ruleVal$toolNum, "-负载随时间变化趋势")) +
#theme_bw() +
# scale_x_continuous(breaks = seq(from = 0, to = max_cc, by = by_interval), label = seq(from = 0, to = max_cc, by = by_interval)) +
scale_y_continuous(labels=scales::percent) +
theme(legend.position = "none",
axis.title.x = element_text(size=30),axis.text.x = element_text(size=30),
axis.title.y = element_text(size=30),axis.text.y = element_text(size=30),
plot.title = element_text(size=30),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(fill="#F4F4F4",color = "#F4F4F4"),
plot.background = element_rect(fill = "#F4F4F4", color = "#F4F4F4"))
# SAVE PLOT
ggsave(filename=fullpath, device = "jpeg", width = 60, height = 20, dpi = 70, limitsize = FALSE, units = "cm")
}
return(path2return)
}
computeStatistics <- function(knife, ruleVal){
# ESTABLISH KM MYSQL CONNECTION
connT <- dbConnect(RMySQL::MySQL(), dbname = "KM", username = SQL_usr, password = SQL_pass, host = SQL_host, port = SQL_port)
on.exit(DBI::dbDisconnect(connT))
# GET TREND DATA
hist <- DBI::dbGetQuery(connT, sqlInterpolate(connT,
"select * from KM.KM_Trend where companyID = ?companyID AND projectID = ?projectID AND machineID = ?machineID AND programNum = ?programNum AND spindleID = ?spindleID AND toolNum = ?toolNum AND time >= ?startTime AND time <= ?endTime",
companyID = ruleVal$companyID, projectID = ruleVal$projectID, machineID = ruleVal$machineID, programNum = ruleVal$programNum, spindleID = ruleVal$spindleID, toolNum = knife, startTime = as.character(ruleVal$startTime), endTime = as.character(ruleVal$endTime)))
hist <- hist[order(hist$time, decreasing = FALSE),]
# IF NOT FOUND, RETURN ERROR
if (nrow(hist) == 0){return(toJSON(list(ret = 44), auto_unbox=TRUE))}
# COMPUTE STATISTICS
tmp <- summary(hist$feature)
# TO LIST
rst <- c(toolNum = knife, min = tmp[['Min.']], q1 = tmp[['1st Qu.']], median = tmp[['Median']], q3 = tmp[['3rd Qu.']], max = tmp[['Max.']])
return(rst)
}
computeSS <- function(knife, dt, spindleID){