This slide was presented at IWIN 2019.

1. Failure Prediction Using Interaction
between Parallel Links of FA Equipment
Masanori Haga (Aichi Institute of Technology)
Kazuhiko Tsutsui (Mitsubishi Electric Co., Ltd.)
Katsuhiko Kaji (Aichi Institute of Technology)
Katsuhiro Naito (Aichi Institute of Technology)
Tadanori Mizuno (Aichi Institute of Technology)
Naoya Chujo (Aichi Institute of Technology)

2. Outline
1. Background and related research
2. Purpose and method of failure prediction
 Fabricated equipment models parallel link
robot
3. Experiment and results
 Peformed PCA
4. Conclusion
2

3. Background
Maintenance cost is major in a whole cost of factory
– Maintenance costs average 15~60% of manufacturing
costs[1]
Features of preventive maintenance
– Maintenance is performed at regular intervals
– Potential for additional maintenance costs
Features of predictive maintenance
– Predicting failure and performing maintenance
when necessary
– Estimated 15~20% reduction in maintenance costs for
aircraft[2]
[1] An Introduction to Predictive Maintenance R. Keith Mobley
[2] AIRLINE MAINTENANCE COST EXECUTIVE COMMENTARY
IATA’s Maintenance Cost Task Force
3

4. Parallel link robot
4
Sensor
Prediction
failure Number of Sensors
 Securing installation space
Sensor
 The same parts are arranged in parallel to
operate final output destination
 Simple mechanism and low manufacturing cost
 Easy maintenance, fast and accurate

5. 5https://www.youtube.com/watch?v=JrDZBOcxsfk

6. Related reaserch1: Diagnostic method
6
Data-driven
− Make predictive model using machine learning from
actual data
− Suitable for complex systems where the governing
equations are unknown
Model-driven
− Make mathematical models for electrical and
mechanical systems
− Be able to compensate for variables that are difficult
to measure by using mathematical model

7. Related research2: Unsupervised learning
7
FA equipment is highly reliable and has few failures
Difficulty to collect failure data
 Principal Component Analysis (PCA)
 Hierarchical clustering
 K-means
 Fuzzy C-means
 Model-based clustering
[3] A Research Study on Unsupervised Machine Learning Algorithms for
Early Fault Detection in Predictive Maintenance
Highest Score

8. Failure prediction using PCA
8
：Normal ：Signs of failure ：Failure
First Principal component
SecondPrincipalComponent
Control chart(image drawing)
Transition from normal to failure
Principal Component Analysis (image drawing)
Near failure

9. Purpose
9
Predicting failure with fewer sensors
using interaction between parallel links
Sensor Information
of Link 1
Information
of Link 2＋
Interaction
Link 1 Link 2

10. Proposed method for failure prediction
Problem of predictive maintenance
– Number of sensors
– Securing installation space
10
Install an abnormal part on a parallel link
Observe from other link
Addressed issue
Question:
Is it possible to observe the abnormality of the whole
equipment and link only from fewer sensors data?

11. Experiment system
11Web cameraPC2
Record
Save
Equipment models parallel link robot
PC1
Control Data (10Hz)
Sensor Data (10Hz)
·Time of rotation (ms) ·Angle (deg)
·Speed of rotation (deg/sec)·Current (mA)
·Temperature (℃) ·Voltage (mV)
15cm
Servo motors
Experimental equipment
12 dimensions(6 dimensions / motor)

12. Observation of interaction between links
 Loading weights are set on joint A
 Sensor data of MA and MB are recorded
 Observe interaction data using PCA
12
Weight (20g)
Servo motor A (MA)
Servo motor B (MB)
Joint A (JA)

13. Experiment procedure
20 minutes measurements and 1 minute intervals at a
fixed time were alternaly performed
 Weight 0g: Normal
 Weight 70g: Fixture+Two weights
 Weight 130g: Fixture+Five weights
13
Weight 0g
Measure
-ment
Add
weight
Interval
• Weight 70g
• Weight 130g
Measure
-ment

14. Video during the experiment
14
MB
MA
Normal 70g 130g

15. Device operation and data points of interest
15
Toward
0°
Toward
60°
P1
P2 P3 P4
Definition of P1, P2, P3, and P4

16. Approximation of data cluster to ellipse
16
Parameters: (x, y), angle, major axis, minor axis
P3
P2
P4
P1
major
minor
(x, y)
angle

17. 12 dimensions (MA and MB)
17
 Cluster P3 shows remarkable transition
 Change in x, angle, major axis, minor axis
P3 P2
P4
P1
(x, y) angle major, minor
Normal (-4.16, 1.63) 3.02° (2.99, 0.65)
70g (-1.84, 1.65) 5.24° (6.34, 0.36)
130g (-1.90, 1.38) 6.66° (6.28, 0.38)

18. 6 dimensions (MA)
18
Change in x, angle
P3
(x, y) angle major, minor
Normal (0.71, -0.94) -0.68° (8.45, 1.80)
70g (0.13, -0.95) 10.9° (7.41, 1.73)
130g (0.02, -0.90) 0.49° (7.47, 1.62)

19. 6 dimensions (MB)
19
Change in x, angle, major axis, minor axis
P3
(x, y) angle major, minor
Normal (4.70, -0.44) -24.5° (2.91, 1.21)
70g (2.30, -0.38) -12.0° (6.12, 0.47)
130g (2.31, -0.42) -12.4° 6.04, 0.51)

20. Discussion
20
Abnormality of the equipment was observed from
the MB data without direct load
 Suggest possibility of total failure prediction with fewer
sensors
Failure prediction algorithm is not completed
 Data changes would be modeled by fine measurement
Future work
 Use higher precision equipment
 Assuming multiple failures

21. Conclusion
Failure prediction using the interaction
between parallel links has been proposed
Fabricated the equipment using
two servo motors and tested
PCA results suggest the possibility of failure
prediction
21

22. Thank you for your attention
22