industrial iot predictive maintenance solution
TRANSCRIPT
© 2017 Cognizant
© 2017 Cognizant
Industrial IoT
Predictive Maintenance Solution
© 2014 Cognizant
Business Challenge
Our Solution
Solution Detail
Benefits
Agenda
© 2014 Cognizant
Business Challenge
Machine and Maintenance Cost
No automated Way to Predict
High Labor Cost
Lost Production Time
Loss in Revenue
Low Yield – Industrial Throughput
© 2014 Cognizant
Our Solution
Predictive Maintenance Solution–
• Real time connectivity with factory floor Sensors to gather real time machine data.
• Machine data is matched against pattern library to understand faulty pattern for a given time interval.
• Any match of faulty pattern in a given time interval, an action is initiated.• Email Notification
• Work Order Initiation in Maximo
• Web Service Initiation
• Sensor data is visualized on Mash zone NextGen.
© 2014 Cognizant
This solution is built using below Integration products and their respective components.
• Software AG – Apama (For Complex Event Processing)
• Software AG – Universal Messaging (To receive events from sensors and send to Apama)
• Software AG – Mashzone NextGen (For visualization of sensor data including fault patterns.)
© 2014 Cognizant
Edge Analytics - Solution Detail
PLC
Controls and monitor
Process Values
OPC UA
OSI PI
Edge Gateway
Edge Analytics
(Apama CEP)
Process Data
Univ
ers
alM
essa
gin
g
JMS
Mashzone
NextGen
Integration Server Data Lake
Ale
rts
Sensor
Graphs
Alarms
Pattern
Detection
Real Time Dashboard
Process Variables of Extruder:
• Extruder pressure
• Extruder Temperature
• Extruder Screw speed
• Product Flow
Process Variable of Coater
• Coater Speed
• Gap between rollers
• Gauge (thickness) of roll
(Output)
Process Data
© 2014 Cognizant
Solution Benefits
Reduction in
Equipment Cost
Reduction in Labor Cost
Reduction in
Production Cost
Increase in Revenue
Increase in Employee Efficiency
New Business Model
© 2014 Cognizant
Event Patterns
Variable Set Point Set Point Range Low Alarm(5% of SP) High Alarm(10% of SP) Static Alarm
Unwinder web tension 1.32 0.8 - 1.5 1.254 1.452 High/Low Unwinder web tension
Extruder Pressure (bar) 150 110 - 190 142.5 165 High/Low Extruder Pressure
Extruder Temperature (°C) 145 110 - 170 137.75 159.5 High/Low Extruder Temperature
Extruder Screw speed (RPM) 120 90-150 114 132 High/Low Extruder Screw speed
Extruder Product Flow (g/s) 2.22 2.0-2.5 2.109 2.442 High/Low Extruder Product Flow
chill roll water temperature 25 17° to 27°C 23.75 27.5 High/Low chill roll water temperature
Coater Speed (RPM) 110 90-150 104.5 121 High/Low Coater Speed
Coater Gap (mm) 0.8 0 .5 - 2 0.76 0.88 High/Low Coater Gap
Winder web tension 1.25 0.8 - 1.5 1.1875 1.375 High/Low Winder web tension
Output Thickness (mm) 0.8 0 .5 - 2 0.76 0.88 High/Low Output Thickness
Effect Pattern Detection for: Current Setup - Labelling Paramters Possible Cause Severity
Wrinkles on the Windup Roll
Cooling (variations)
Inadequate tension control at
the rewind
Coater chill roll water temperature -
Variations(High and Low alarms of the SP)
Winder web tension - low alarm of the SP
In 60 second time window
Water cooling passages
clogged
Idler rollers not in train High/Low
Fault Pattern
Threshold Breach
© 2014 Cognizant
Edge Analytics – Sensor Feeds
© 2014 Cognizant
Edge Analytics – Alarms