Improving Industrial Machine Support Using InfluxDB, Web SCADA, and AWS

Engineering the Next Generation
of Process Technologies
Andrew Smith
Lead Innovation Engineer
Connected Support

Bio • Control & Systems Engineer
• Chartered in 2003
• Started in Process Control in 1995
• glass manufacture,
• water & wastewater treatment
• SCADA, PLC control
• Data Mining, Modelling
• 12 years overseas
• Tech Start-up,
• Manufacturing,
• Innovation Consultancy
• Last 3 years in Machine Manufacturing, IIoT and
Process Data
• Now Lead Innovation Engineer for LBBC
• Live in Leeds, UK

Connected Support
• LBBC’s machines & processes
• The value of Connected Support
• Data source
• Typical users
• Typical scenarios
• Requirements
• Architecture
• Data shape as it flows
• InfluxDB role and use
• Troubleshooting
• Data mining & processing

Dewaxing Boilerclave® system
Investment Casting equipment (Global)
Ceramic Core Leaching system
Investment Casting: The process of forming a casting
(e.g. turbine blade) from a wax “pattern”, a ceramic
“core”, and a ceramic “shell”

The value of “Connected Support”
Hear what customers hear, when they hear it:
Taking real-time alerts beyond the foundry fence.
See what customers see: Taking data insights beyond the foundry fence
live and historical for the life of the machine
Advanced data processing: Complex calculations on
combinations of multiple variables at regular intervals
Data driven Condition monitoring: Spotting gradual/ imperceptible
changes and alerting the right people

Where does the data come from ? • Heating
• Top Band %
• Mid Band %
• Bottom Band %
• Fault Codes
• Valves
• V1 Open/Closed
• …
• V7 Open/Closed
• Pumps
• M1 running
• M2 Running
• Cycle
• Cycle No
• State No
• Phase No
• Countdown
• Lid / Lock
• Open / Closed
• Locked / Unlocked
• Shotbolt Engaged
• Pressures
• Process Air
• Vessel
• Temperatures
• Top Band
• Mid Band
• Bottom Band
• Top Vessel
• Mid Vessel
• Bottom Vessel
• Levels
• KOH
• Vessel
LC450
Core
Leacher
Industrial PLC (control)

• Cost efficiency ( ingest, query, storage )
• Use of standard hardware between PLC & Cloud
• Security:
• Encryption of data in transit
• MFA for data in storage
• Reliability:
• Not loosing data (even if cloud systems are down)
• Being able to guarantee that data arrived
• Serverless infrastructure (not even EC2)
• Data recording for lifetime of machine
• Data visualisation & exploration for the inexpert (ideally
without writing any code or using known languages)
• Live dashboards
• Alerts to Mobile App, email, SMS
• Complex data processing
• On data triggers
• On schedule
Needs &
Requirement

Typical Users
Maintenance Engineers
• Electrical / Mechanical background
• No software experience
• Limited PC interest
• Responsible for finding and fixing issues
• Contacted by customer with issues / questions
• Investigate issues using data
Process experts
• Specialist background. Experts in their field
• PC competent
• Obsessed about improving the process
• Called on for challenging process / maintenance issues
Process-Data people
• Data obsessed
• All about performance, KPIs
• Six-Sigma “blackbelt” types
• Excel experts
• Not necessarily programmers
• Create Statistical queries
Database / Software people
• Come from software background
• SQL experience
• Build Visualisations / dashboards
Managers
• Need quick easy to understand visuals for decisions
• Have very little time
• Unlikely to be bothered to point and click, let alone type
Users / Customers
• Observe issues
• Ask questions. Have problems. Want answers
• Don’t understand databases

Typical Scenarios
Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
I’m seeing this.
Something has gone
wrong. It’s not working
Fix it!
Process Data Geeks
• Data obsessed
• Excel experts

Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
I want to visualise this
data together to
troubleshoot what
happened when
Process Data Geeks
• Data obsessed
• Excel experts
Typical Scenarios

Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
Here’s what I have
discovered (with notes
and annotations)
Process Data Geeks
• Data obsessed
• Excel experts
Typical Scenarios

Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
I’m seeing a valve
oscillation when the
vessel pressurises. Is
this a problem? Has it
always happened?
Does it happen on
other machines?
Process Data Geeks
• Data obsessed
• Excel experts
Typical Scenarios

Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
I need to investigate
this further before I
answer that. I need
data.
Process Data Geeks
• Data obsessed
• Excel experts
Typical Scenarios

Process experts
• PC competent
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
I want to visualise
Overall Equipment
Availability for each
machine in a process /
subprocess
Process Data Geeks
• Data obsessed
• Excel experts
Typical Scenarios

Process experts
• PC competent
Process Data Geeks
• Data obsessed
• Excel experts
Database Geeks
• SQL experience
Managers
Users / Customers
• Observe issues
• Need access to just their data
I’m discovering some
hidden patterns in
data.
• There is an
opportunity OR
• There is a problem
Typical Scenarios

• Cost efficiency ( ingest, query, storage )
• Use of standard gateways between PLC & Cloud
• Security:
• Encryption of data in transit
• MFA for data in storage
• Reliability:
• Not loosing data (even if cloud systems are down)
• Being able to guarantee that data arrived
• Serverless infrastructure (not even EC2)
• Data recording for lifetime of machine
• Data visualisation & exploration for the inexpert (ideally
without writing any code or using known languages)
• Live dashboards
• Alerts to Mobile App, email, SMS
• Complex data processing
• On data triggers
• On schedule
Needs &
Requirement

Our chosen infrastructure • Gateways speak MQTT & OPC

• AWS is MQTT broker

• MQTT messages handled by
Lambda functions
• Lambda code
• Updates InfluxDB
• Updates live dashboards
(Servitly)

Lambda functions
• Lambda code
(Servitly)
• Failed data is queued and
stored in DynamoDB (e.g. if
InfluxDB down)

Lambda functions
• Lambda code
(Servitly)
InfluxDB down)
• Serverless code is watched
and alarms on issues

Lambda functions
• Lambda code
(Servitly)
InfluxDB down)
• Serverless code is watched
and alarms on issues
• 3rd party (Servitly) used for
Alerts / Live dashboarding /
Customer’s own view

Data shape [
{
"S7-1200/DB20/Feedwater_Temperature": 79.939598,
"time": "2020-07-28T12:32:07.000Z"
},
{
"S7-1200/MainProgram/HMIBoilerLevel": 16417,
"time": "2020-07-28T12:32:07.000Z"
},
{
"Dewax_Pressure": 9.36146,
"time": "2020-07-28T12:32:07.000Z"
},
{
"Dewax_Temperature": 178.315674,
"time": "2020-07-28T12:32:07.000Z"
},
{
"Boiler_Pressure": 9.394047,
"time": "2020-07-28T12:32:07.000Z"
},
{
"int_PLC_Status": 706507,
"time": "2020-07-28T12:32:07.000Z"
},
{
"Boiler_Temperature": 181.019211,
"time": "2020-07-28T12:32:08.000Z"
},
{
"time": "2020-07-28T12:32:08.000Z"
},
{
"time": "2020-07-28T12:32:08.000Z"
},
{
"time": "2020-07-28T12:32:08.000Z"
},
{
"time": "2020-07-28T12:32:08.000Z"

Data shape {
"clientID": "1701",
"payload": [
{
"time": "2020-07-28T12:32:07.000Z"
},
{
"time": "2020-07-28T12:32:07.000Z"
},
{
"time": "2020-07-28T12:32:07.000Z"
},
{
"time": "2020-07-28T12:32:07.000Z"
},
...
...
...
...

Data shape {
"clientID": "1701",
"type": "data",
"intermediate": [
{
"ts": 1663666327000,
"data": {
"Feedwater_Temperature": 79.939598,
"HMIBoilerLevel": 16417,
"apparent_data_age_minutes_difference": -0.1
}
},
{
"ts": 1663666328000,
"data": {
"Boiler_Pressure": 9.390972
}
},
{
"ts": 1663666329000,
"data": {
"int_PLC_Status": 706831
}
},
{
"ts": 1663666330000,
"data": {

Data shape [
'data,host=1701,method=b1_mqtt_to_influx
Feedwater_Temperature=79.939598,HMIBoilerLevel=
16417,Dewax_Pressure=9.36146,Dewax_Temperature=
178.315674,Boiler_Pressure=9.394047,int_PLC_Status=
706507,apparent_data_age_minutes_difference=-0.1
1663666327000',
'data,host=1701,method=b1_mqtt_to_influx Boiler_Temperature=
181.019211,Feedwater_Temperature=79.867264,HMIBoilerLevel=
33,Dewax_Temperature=178.324722,int_PLC_Status=
706704,Dewax_Pressure=9.357156,Boiler_Pressure=9.390972
1663666328000',
178.306625,Boiler_Pressure=9.387898,int_PLC_Status=706831
1663666329000',
178.27951,Boiler_Pressure=9.387898,int_PLC_Status=706850
1663666330000',
'data,host=1701,method=b1_mqtt_to_influx Tag7=
0,Blowdown_Water_Supply_NRV002_OPEN=False,Tag8=
0,Boiler_Temperature=180.992081,Tag9=0,State_Register=
9,reqBoilerBlowdown=False,string_Client_IP="10.0.0.90",Boiler_Blo
wdown_Drain_OPEN=False,srWaxboothNotClosed=False,Steam_Ex
_COndensate_Drain_MV007_OPEN=False,Heaters_4_To_6
=False,Blowdown_Drain_OPEN=False,Boiler_Blowdown_Isolation_
MV005_OPEN=False,Open_Wtr_Tnk_Fill_Valve=False,Heaters_9
_To_10=False,Boilerclave_Exhaust_OPEN=False,
Feedwater_Temperature=79.98481,HMIBoilerLevel=16417,
Dewax_Pressure=9.355311,Boiler_Blowdown_Isolation_MV006
_OPEN=False,opflagOpenFeedwaterFillValve=False,Dewax_Tempera
ture=178.261429,Wax_Steam_Isolation_MV008
_OPEN=False,lsSteamValve1Open=True,Boiler_Pressure=
9.390972,srDoorClosed=True 1663666331000'
]

Data shape {
"pub_topic": "LBBC_STC/1701/data",
"ClientID": "wyxcvrbeku",
"mid": 1,
"payload": "{'ts': 1663666327000, 'data':
{'Feedwater_Temperature': 79.939598, 'HMIBoilerLevel': 16417,
'Dewax_Pressure': 9.36146, 'Dewax_Temperature': 178.315674,
'Boiler_Pressure': 9.394047, 'int_PLC_Status': 706507,
'apparent_data_age_minutes_difference': -0.1}}",
"unique_id": "wyxcvrbeku_001_1",
"item_id": "623fb913-6fca-45d6-8e89-496c52b5e86b"
}
{
"mid": 2,
"payload": "{'ts': 1663666328000, 'data': {'Boiler_Temperature':
181.019211, 'Feedwater_Temperature': 79.867264, 'HMIBoilerLevel':
33, 'Dewax_Temperature': 178.324722, 'int_PLC_Status': 706704,
'Dewax_Pressure': 9.357156, 'Boiler_Pressure': 9.390972}}",
}
{
"mid": 3,
16417, 'Dewax_Pressure': 9.355311, 'Dewax_Temperature':
178.306625, 'Boiler_Pressure': 9.387898, 'int_PLC_Status':
706831}}",
}
{
"mid": 4,

Connected support procedure:
Notification: LBBC
and Customer are
notified of an Alert
Service team attempt
to diagnose using just
the dashboard. InfluxDB is used to
construct any detailed
analysis

Value of InfluxDB to us
Value for IIoT use case
What
InfluxDB
delivers
Low Cost
Performant
ingest, query, process
Visualise data

Process
data
Low Cost
Performant
Visualise data
Automate
What
InfluxDB
delivers

Process
data
Low Cost
Performant
Visualise data
Automate Performant UI
Share insights
Explore data
Security
What
InfluxDB
delivers

What other products did we consider?
Forecast of DATA VOLUMES
Forecast of INGEST + STORE COSTS

Background:
• Our “Core Leaching” product uses Hydroxide
• Hydroxide is a dangerous & costly chemical
• Hydroxide depletes over months to Silicates
• Both are high pH so measuring pH doesn’t work
• Measuring Hydroxide has a customer value
Chemistry & Physics:
• Hydroxide has a very low vapour pressure.
• Low hydroxide à high vapour pressure
Data:
• 1 year’s worth of cycle data
• Pressures, temperatures, valves, pumps etc
• Data includes both fresh and spent Hydroxide
Challenge :
• Estimate Hydroxide strength from process data
• Predict when Hydroxide is going to be exhausted
Data Exploration Example

Estimating Hydroxide Vapour Pressure for cycle 1247
Cycle # 1247

Flux Code
import "math"
from(bucket: "PLC_Router_Data")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["host"] =~ /1718/)
|> filter(fn: (r) => r["_field"] == "VslPress1Bar" or r["_field"] == "State" or r["_field"] == "CycleNumber" or
r["_field"] =~ /InletVlvS/ or r["_field"] =~ /SafetyVlvS/)
|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
|> group()
|> fill(column: "SafetyVlvStatus", usePrevious: true)
|> fill(column: "InletVlvStatus", usePrevious: true)
|> fill(column: "VslPress1Bar", usePrevious: true)
|> fill(column: "State", usePrevious: true)
|> fill(column: "CycleNumber", usePrevious: true)
|> map(fn: (r) => ({ r with SafetyValve2: r.SafetyVlvStatus }))
|> increase(columns: ["SafetyVlvStatus"])
|> difference(nonNegative: true, columns: ["SafetyVlvStatus"])
|> cumulativeSum(columns: ["SafetyVlvStatus"])
|> filter(fn: (r) => r.State == 26)
|> filter(fn: (r) => r.SafetyValve2 == 0)
|> group(columns: ["SafetyVlvStatus","CycleNumber"], mode:"by")
|> sort(columns: ["_time"], desc: false)
|> map(fn: (r) => ({ r with _time_delta: int(v: r._time)/1000000000 }))
|> increase(columns: ["_time_delta"])
|> map(fn: (r) => ({ r with time_till_next: r._time_delta }))
|> sort(columns: ["_time_delta"], desc: true)
|> difference(nonNegative: false, columns: ["time_till_next"])
//|> filter(fn: (r) => r._time_delta >= 15)
|> filter(fn: (r) => r._time_delta <= 90)
|> min(column: "time_till_next")
|> sort(columns: ["_time"], desc: false)
|> group(columns: ["CycleNumber"], mode:"by")
|> increase(columns: ["SafetyVlvStatus"])
|> filter(fn: (r) => r.SafetyVlvStatus >= 5)
Select
Pivot + Fill
Computation
of auxiliary
filter variables
Computation
of auxiliary
filter variables

Flux Code
|> rename(columns: {SafetyVlvStatus: "_x", VslPress1Bar: "_y"})
|> map(fn: (r) =>
({r with x_sq: r._x * r._x}))
|> map(fn: (r) =>
({r with x_y: r._x * r._y}))
|> reduce(fn: (r, accumulator) =>
({
sum_x: r._x + accumulator.sum_x,
sum_x_sq: r.x_sq + accumulator.sum_x_sq,
sum_xy: r.x_y + accumulator.sum_xy,
sum_y: r._y + accumulator.sum_y,
sum_n: 1.0 + accumulator.sum_n,
last_time: if r._time > accumulator.last_time then r._time else accumulator.last_time,
}), identity: {
sum_x: 0.0,
sum_x_sq: 0.0,
sum_xy: 0.0,
sum_y: 0.0,
sum_n: 0.0,
last_time: time(v: 0),
})
|> map(fn: (r) =>
({r with _m: r.sum_n * r.sum_xy - (r.sum_x * r.sum_y)}))
|> map(fn: (r) =>
({r with _m: r._m / (r.sum_n * r.sum_x_sq - (r.sum_x * r.sum_x))}))
|> map(fn: (r) =>
({r with _b: r.sum_y - (r._m * r.sum_x)}))
|> map(fn: (r) =>
({r with _b: r._b / r.sum_n}))
|> map(fn: (r) =>
//({r with _value: math.round(x: 100.0 * (r._m * 5.0 + r._b))/100.0}))
({r with _value: math.round(x: 100.0 * (r._b))/100.0}))
|> rename(columns: {last_time: "_time"})
|> group()
|> map(fn: (r) => ({ r with _colour: r._value}))
|> sort(columns: ["_value"], desc: false)
|> keep(columns: ["_time", "CycleNumber", "_value", "_colour"])
|> filter(fn: (r) => r._value > 0 or r._value < 0)
Set up
regression
Calculation
regression
SUM terms
using
“Reduce”
Calculation of
Y = mx + c
Preparing for
display
𝑚 =
𝑛 ∑ 𝑥𝑦 − ∑ 𝑥 ∑ 𝑥𝑦
𝑛 ∑ 𝑥! − (∑ 𝑥)
!
𝑏 =
∑ 𝑦 − 𝑚 ∑ 𝑥
𝑛

We’re going to do that
with a lot of data:
• 12 months data
• 150 cycles
• 1.3 million points
Computed and visualised in < 10 secs by
the cloud database.
Data Processing & Visualisation:

Discovery of hidden process patterns with value to customer!
“your timing is
right on cue. I
thought it was
close to spent.
Our performance
is definitely
decreasing”
Feb 22nd
LBBC notify
customer
that
Hydroxide
needs
changing
New
Hydroxide
Cycle
#
1247
New
Hydroxide

Tips / Learning (opinion only)
Suggest against Telegraph for IIoT. AWS+Lambda+Influx API is a ‘better’ solution:
• Telegraph needs a host. That means hardware or EC2
• AWS is serverless. AWS infrastructure costs £10/month for millions of msgs!
• AWS Lambda scales with data rates. Parallel instances are added as needed.
Suggest against MQTT ingest for IIoT. AWS+Lambda+Influx API is still a ‘better’ solution:
• If InfluxDB is down, you’ll need more advanced store and forward than MQTT offers
• Most IIoT solutions will need data to go to multiple endpoints and verify it got there.
Direct MQTT from the industrial gateway to InfluxDB will not do that
For IIoT, use “measurement” as your hostname or equipment name.
“measurement” as a data type doesn’t make sense for the IIoT use case.
• Equipment name is a more natural IIoT primary key (it’s usually the first filter of data)
• Influx’s Data explorer doesn’t allow the exploration of multiple “measurements”. i.e. you can’t
use it to plot temperatures, pressures and Boolean values on the same chart

We love InfluxDB, BUT …
It took a lot of effort to put together an AW + Lambda + Influx API.
If only Influx gave users a pre-prepared, fully functional cloud integration (AWS, GCP, Azure)
InfluxDB doesn’t allow us to restrict logins to access only a subset of data.
Users are all ‘owners’ and can view (and delete) the entire database.
We love that a link can be shared to a dashboard where time (start;stop) & variables are in the https.
But this doesn’t work for Notebooks (and neither do variables)
The InfluxDB UI delivers most of what the Industrial IoT / Process Control use case demands, with some
notable exceptions that reduce usefulness in process troubleshooting & exploration:
• The display of Boolean values
• Synchronisation of multiple charts (zoom + pan together)
• X-Y plots need Flux code.
Username / Password is basic … But IIoT users probably need to protect their data from view by MFA

• 12 months data
• 150 cycles
• 1.3 million points
Computed and visualised in < 10 secs by
the cloud database.
Data Processing & Visualisation:

Improving Industrial Machine Support Using InfluxDB, Web SCADA, and AWS

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