PREDICTIVE MAINTENANCE OF AGILON ... - communities.sas… · 2 Mikael Björkbom, Sami Terho...

PREDICTIVE

MAINTENANCE OF

AGILON AUTOMATIC

WAREHOUSE

Mikael Björkbom, Sami Terho

© 2016 Konecranes. All rights reserved.24.5.2017Mikael Björkbom, Sami Terho2

© 2016 Konecranes. All rights reserved.

TRUCONNECT Remote Monitoring

• Crane maintenance information through

YourKonecranes.com portal– Usage data

– Maintenance data

– Asset details

TRUCONNECT Remote Support

• 24/7 remote problem solving and troubleshooting

REMOTE MONITORING AND SUPPORT


AGILON

24.5.2017Mikael Björkbom, Sami Terho4


AGILON MATERIAL

HANDLING SOLUTION

• Material handling solution

patented by Konecranes

• Storing boxes with maximum

size 600 x 400 x 450 mm and

weight 25 kg

• Includes shelves, storage

robot, and support services



• Agilon monitors the transfer and weights of the packets and keeps inventory

• Information can be remotely accessed and delivered for example to suppliers

• Automatic alerts of low inventory can be sent to buyers

UP-TO-DATE INFORMATION OF MATERIAL

AVAILABILITY



• For a monthly fee, customer

gets a complete working

system

• Maintenance of the system is

included in the price

• Remote monitoring and

maintenance to allow quick

response to possible problems

MAINTENANCE SERVICE



PREDICTIVE

MAINTENANCE CASE

• Predict failure in advance

– Prepare for component change

– Increase Agilon availability and

customer productivity

• Analyzing data from motor

torques

– Finding faulty motors

– Increased friction

– Mechanical problems



PREDICTIVE

MAINTENANCE CASE

• Predict failure in advance

– Prepare for component change

– Increase Agilon availability and

customer productivity

• Analyzing data from motor

torques

– Finding faulty motors

– Increased friction

– Mechanical problems


Motors for dockingthe capsule andgripping the packet

Motors for

moving along

rail and lifting

the capsule


ANALYTICS ENVIRONMENT AT KONECRANES



• Measured data from electric motors

• Error messages

• Operation statistics

• Service data

– Incident descriptions

– Service tickets

– Failure analyses

DATA USED FOR ANALYSIS



APPROACH TO THE PROBLEM



• Get data

– If first case: CSV dumps from database…

– Direct database connection

• Understand data

– Data quality

– Joining different data sources

– Finding failure cases

• Define problem and modeling target

– Predict days or cycles to failure

– Predict if failure will occur soon

PREPARATION



• Analytics Base Table– Time-resolution: day

– Motordata• 2-4 motors for each movement: combine measurements: mean, min, max, range…

• Daily features: mean, min, max, P5, P95, range…

• Change of values

• ~100 features

– Error messages• Categorize: ~20 categories

• Sum counts by day

• Moving average to include history

– Operation statistics already by day

• Target– Manually from service tickets

PREPROCESSING AND FEATURE EXTRACTION



• Finding example cases for target variable

– Service data written by humans

– Can this failure be seen in the available data?

– Manual review of service data -> select target cases

• Typical for predictive maintenance, since data not intended for machine readability

• Hybrid approach

– Manually verified cases: 10x weight

– Automatic cases based on data

• Only few certain failures identified

PREDICTIVE MAINTENANCE EXAMPLE CASES



• Smoothing (low-pass filter) of selected data to get near history effect (”LTM”)

• Remove data from ”uncertain period”– Improves modeling performance and ”early” false alerts

– Avoids filtering out variables that ”do not correlate” with target

PREPROCESSING TRIX



MODEL DEVELOPMENT SUMMARY



Worked excellent with failure case But bad with non-failure case

V1: IS THIS POSSIBLE?



• Better features

– Normalization of data

– Days to failure (continuous) → Fail soon (binary)

– Manually verified cases (10x weight)

V2: NEW ATTEMPT



V2: FINDING NEW/VERIFYING UNCERTAIN CASES



• Force normalize data from all motors

• Not enough failure cases to model each individual motor

– Predict replacement: Capsule or Robot

• ”Something in capsule/robot is broken”

– Joint daily features from all motors

V3: GENERALIZE TO ALL MOTORS



CAPSULE PREDICTION RESULTS



SOME MISCLASSIFICATIONS STILL



• Manual field test validation

– One round of data update

– First successful failure prediction!

• User interface

– View prediction results

– Underlying data

– Component replacement decision

• Automating data update process

– New data -> Scoring -> Present

• Automating model update

TAKING INTO USE



MODEL DEVELOPMENT SUMMARY



DEVELOPMENT PROCESS



• Predictive maintenance case

– Typically data entered by humans

– Can the failure be seen in the data?

• Don’t assume things from the data

– Data end does not mean failure: Logging was ended for other reason

– Component change without obvious reason: capsule is also changed when robot is changed

• Some manual work can make the difference

• Few good failure cases

– Few good example cases to steer the model in right direction

– Use model to find the rest of the cases

• Ask from experts

– Learn everything they know

– Success is in the features

LEARNINGS



NOT JUST LIFTING

THINGS, BUT

ENTIRE BUSINESSES


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PREDICTIVE MAINTENANCE OF AGILON ... - communities.sas… · 2 Mikael Björkbom, Sami Terho...

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