Transforming raw machine data into real business outcomes such as OE and OEE is a journey. In this talk we will learn some tips and tricks for analyzing and transforming your machine data using InfluxDB and other third-party platforms.

2. Tips & Tricks for
Analyzing IIoT in
Real-Time
Jay Clifford
Developer Advocate, InfluxData

3. Transforming raw machine data into real business
outcomes such as OE and OEE is a journey. In this talk
we will learn some tips and tricks for analysing and
transforming your machine data using InfluxDB and other
third party platforms.
Jay Clifford
Developer Advocate, InfluxData
Jay Clifford is a Developer Advocate for InfluxData.
Before joining InfluxData he previously specialised in
solving industrial pain points using Vision AI and OT
connectivity. Jay now uses his experience within the IoT
and automation sector to enable developers and
industrial customers alike to realise the potential of Time
Series data and analytics.
Tips & Tricks for Analyzing
IIoT in Real-Time

4. Agenda
• Data Collection
• Data Storage
• Data in Action

5. Machines Data Collection Data Storage Data in action
Site A
Global
Custom Apps
Third Party
Visualization

6. Data Collection

7. Telegraf the middleman
Direct
Middleman
Preprocessing of data + preliminary forecasting
• Converting metrics
• Aggregation
• Forecasting
Processor +
Aggregator Plugins

8. [[processors.enum]]
[[processors.enum.mapping]]
field = "status_code"
dest = "status"
[processors.enum.mapping.value
_mappings]
0 = OFF
1 = ON
2 = RUNNING
3 = FAULT
Converting
2
3
[[aggregators.basicstats]]
period = "30s"
drop_original = false
stats = ["count","diff",
"rate","min",
"max","mean",
"non_negative_diff",
"non_negative_rate","
stdev","s2","sum","interval"]
Aggregation
[[processors.execd]]
command = ["python",
"./processors/forecasting.py"]
Forecasting

9. Data Storage

10. Time in state
Calculating state duration
from(bucket: "machines")
|> range(start: -24h)
|> filter(fn: (r) => r["_measurement"] == "machinery")
|> filter(fn: (r) => r["_field"] == "state")
|> events.duration(unit: 1h, columnName: "duration")
|> group(columns: ["_value", "_start", "_stop", "machine_id"])
|> sum(column: "duration")
|> pivot(rowKey: ["_stop"], columnKey: ["_value"], valueColumn: "duration")
|> map(
fn: (r) => {
totalTime = float(v: r.RUNNING + r.FAULT)
return {r with FAULT: float(v: r.FAULT) / totalTime * 100.0,
RUNNING: float(v: r.RUNNING) / totalTime * 100.0}
},
) FAULT RUNNING
15 85

11. Determine a state
Deriving state
import "math"
from(bucket: "machines")
|> range(start: -24h)
|> filter(fn: (r) => r["_measurement"] == "machinery")
|> filter(fn: (r) => r["_field"] == "vib" or r["_field"]
== "vibTarget")
|> aggregateWindow(every: 12h, fn: mean)
|> pivot(rowKey:["_time"], columnKey: ["_field"],
valueColumn: "_value")
|> map(fn: (r) => ({ r with vibrationDiff: r.vib -
r.vib_target }))
|> map(fn: (r) => ({ r with status: if math.abs(x:
r.vibrationDiff) >= 15.0 then FAULT else RUNNING }))
|> drop(columns: ["vib", "vibTarget"]) _time status vibrationDiff
### FAULT 17.435

12. Anomaly Detection
Comparing machine behavior
import "contrib/anaisdg/anomalydetection"
from(bucket: "machines")
|> range(start: -24h)
|> filter(fn: (r) => r["_measurement"] == "machinery")
|> filter(fn: (r) => r["_field"] == "vib"
|> anomalydetection.mad(threshold: 5.0)
|> filter(fn: (r) => r["level"] == "anomaly")
|> group(columns: ["machineID"], mode: "by")
|> aggregateWindow(every: 1m, fn: count, createEmpty:
false)
_time
machine
ID
_value # level
### CNC1 2.1 # normal
### CNC2 3.4 # Anomaly
### CNC3 2.1 # normal

13. Edge to cloud
Site A Site A analysed
Global
Edge
Cloud
• Durability
• Visibility
• Security
• Performance

14. Data in Action

15. Site A Site A analysed
Edge
Cloud
Global
Global
Analysed

16. Learn more

17. T H A N K Y O U

18. Converting Aggregation Forecasting
[[processors.enum]]
[[processors.enum.mapping]]
field = "status_code"
dest = "status"
[processors.enum.mapping.value
_mappings]
0 = OFF
1 = ON
2 = RUNNING
3 = FAULT
[[aggregators.basicstats]]
period = "30s"
drop_original = false
stats = ["count","diff",
"rate","min",
"max","mean",
"non_negative_diff",
"non_negative_rate","
stdev","s2","sum","interval"]
[[processors.execd]]
command = ["python",
"./processors/forecasting.py
"]
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