Robot Predictive Maintenance with Real-Time Sensor Data and Machine Learning
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Our Strata Beijing 2017 presentation slides where we show how to use data from a movement sensor, in real-time, to do anomaly detection at scale using standard enterprise big data software.
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Robot Predictive Maintenance with Real-Time Sensor Data and Machine Learning
- 1. © 2017 MapR TechnologiesMapR Confidential 1 State of the Art Robot Predictive Maintenance with Real-time Sensor Data Mateusz Dymczyk, Software Engineer @ h2o.ai Mathieu Dumoulin, Data Engineer @ MapR Strata New York 2017
- 2. © 2017 MapR TechnologiesMapR Confidential 2 State of the Art Robot Predictive Maintenance with Real-time Sensor Data Part 2 Mateusz Dymczyk, Software Engineer @ h2o.ai Mathieu Dumoulin, Data Engineer @ MapR Strata New York 2017
- 3. © 2017 MapR Technologies 3 Mateusz Dymczyk and Mathieu Dumoulin • Data Engineer @ MapR Technologies • Previously data scientist, DS manager, search, NLP and ML engineering Canada and in Japan • Software Engineer @ H2O.ai • Previously ML/NLP @ Fujitsu Laboratories and en-japan inc
- 4. © 2017 MapR Technologies • 907B$/y investment until 20201 • 1,6M operational industrial robots in the world in 20152 • 2.6M by 20201 1: What Everyone Must Know About Industry 4.0, Forbes June 2016 2: International Federation of Robotics (IFR) study World Robotics 2016source: PwC 2016 Global Industry 4.0 Survey Industry 4.0 is Now Industry 4.0 systems1: 1. Interoperable 2. Information transparency 3. Technical assistance 4. Decentralized decision making
- 5. © 2017 MapR Technologies 5 Predictive Maintenance for Industrial Robots Primary goal: Reduce unplanned downtime
- 6. © 2017 MapR Technologies Robot Actuator Failure Prediction PoC Model 6-axis industrial robot LPMS-B2 Wireless movement sensor PoC Goal: Predict potential actuator failure in real-time (within 3s)
- 7. © 2017 MapR Technologies 7 Success criteria • Detect correct robot state (Normal/Failure) within in 3s • Recall > precision • Improve over time once a “MVP” model is working Photo: Ambient Intelligence Blog
- 8. © 2017 MapR Technologies 8 Need for Scale: Deploy to a Real Factory Tesla Factory photo by Paul Sakuma/AP
- 9. © 2017 MapR Technologies Don’t Reinvent the Wheel • We have limited time and bugdet for this PoC • Tools > assembly of existing software > coding • The state of the art is often OSS anyways!
- 10. © 2017 MapR Technologies 10 Video of solution in action 2m
- 11. © 2017 MapR Technologies PoC Building Blocs People: 2 Engineers LP-RESEARCH, ML Engineer and Data Engineer Effort: 2 months part-time
- 12. © 2017 MapR Technologies 12 Experimental Setup
- 13. © 2017 MapR Technologies 13 Experimental Setup: Normal State
- 14. © 2017 MapR Technologies 14 Experimental Setup: Failure State
- 15. © 2017 MapR Technologies Anomaly Detection for Predictive Maintenance
- 16. © 2017 MapR TechnologiesMapR Confidential 16 Machine Learning Project Flow Explore and Analyze Choose Algorithm Build Model Evaluate Model Put into production Problem evaluation & definition Data preparation
- 17. © 2017 MapR Technologies 1. Starting Point – Classification problem – Time series data • Linear Acceleration X, Y, Z axis – No labeled data at first • Accumulate over time 2. Machine Learning goal/metrics – Recall vs. Precision 3. Additional Requirements – Detect state within 3 seconds Problem Definition Normal State (OK!) PREDICT FAILURE
- 18. © 2017 MapR Technologies 18 Data Source: Movement Sensor • Real-time, on-device calculation of linear acceleration – Data centered around 0 – Measurements [-1,1] • Data output rates of up to 400Hz • Very sensitive www.lp-research.com LPMS-B2
- 19. © 2017 MapR Technologies 19 Sensor Data Preparation 200ms window Ref: 21 Great Articles and Tutorials on Time Series • Feature selection(3 / 27 features) • Windowing – Window size: 200ms – Sensor data rate: 100Hz
- 20. © 2017 MapR Technologies 20 Modeling for Anomaly Detection • Unlabeled data -> unsupervised learning • Training data consists only of data during “normal state” runs – Only train on normal op. data • Conclusion: anomaly detection • Possible algorithms: – HMM – Autoencoders – LSTM auto encoders – KNN, Local Outlier Factor Anomaly Detection Get Ted Dunning’s Anomaly Dectection Book Anomaly!
- 21. © 2017 MapR Technologies First Model: Autoencoders • A kind of neural network used for unsupervised learning of efficient codings • Requires a training pass to learn a representation of ”normal” data • Anomalous data will have a large reconstruction error compared to normal data Längkvist, Martin, Lars Karlsson, and Amy Loutfi. "A review of unsupervised feature learning and deep learning for time-series modeling." Pattern Recognition Letters 42 (2014): 11-24.
- 22. © 2017 MapR Technologies 22 Experimental Setup: Training the Model
- 23. © 2017 MapR Technologies Performance Evaluation • Evaluation dataset – Captured from a preprogrammed “pre-failure” operation mode – 1x full movement cycle of (“pre-failure”) labeled data • Normal 90% Failure 10% • Performance measures: – MSE during training – TPR/FPR on the test dataset Note: For an example with code: https://machinelearningmastery.com
- 24. © 2017 MapR Technologies 24 ML – Results Note: Time window: 200ms, Threshold: 2SD
- 25. © 2017 MapR Technologies 25 Experimental Setup: Real-time Predictions
- 26. © 2017 MapR Technologies Next Step: Long Short Term Memory (LSTM) • Deep learning architecture in the RNN family that remembers arbitrary intervals1. • Overcomes known RNN issues – limited memory – instability • Especially used for image, text and speech applications … and time series data Ref: “Understanding LSTM Networks” by Christopher Olah (2015) 1: Long Short-Term Memory, Hochreiter and Schmidhuber (1997) RNN LSTM
- 27. © 2017 MapR Technologies Implementation: Keras with TensorFlow Backend • Similar design to Autoencoder • Encoder and decoder are separate • Model implemented with Keras in Python but executed by H2O Deep Water
- 28. © 2017 MapR Technologies LSTM and H2O: Deep Water • Keras model is trained through H2O – Fast data ingest, missing value handling, ignoring columns, etc. 2.5m/100 epoch – MOJO output (binary model representation) • Usable from any JVM language • Just like H2O POJO! • Prediction service infrastructure is reused
- 29. © 2017 MapR Technologies LSTM Results LinAccX Results LinAccZ LinAccY
- 30. © 2017 MapR Technologies 30 Conclusion
- 31. © 2017 MapR Technologies What We Didn’t Talk About (Much) Security: System and Data Reliability and Scalability Machine learning logistics Integration in a Factory
- 32. © 2017 MapR Technologies 32 • Clever assembly of existing enterprise software can do it with surprisingly small time, effort and complexity • H2O and MapR offers a fast path to value for production ML • LSTM doesn’t easily beat Autoencoders without significant effort and expertise • Converged platforms reduce complexity Advanced Predictive Maintenance Poster by J. Howard Miller (1943)
- 33. © 2017 MapR TechnologiesMapR Confidential New: Machine Learning Logistics Model Management in the Real World O’Reilly book by Ellen Friedman & Ted Dunning © Sept 2017 Get free pdf copy of book courtesy of MapR: https://mapr.com/ebook/machine-learning-logistics/ Visit MapR booth for free book signings & booth theater presentations by the authors Wed schedule: Book signing: afternoon break 3:35 – 4:20 pm Booth presentation by Ted Dunning: 3:00 – 3:30 pm Thur schedule: Book signing: morning break 10:45 – 11:20 am Booth presentation by Ellen Friedman: 3:00 – 3:30 pm
- 34. © 2017 MapR Technologies 34 Q&A ENGAGE WITH US mateusz@h2o.ai mathieu.dumoulin@mapr.com PROJECT GITHUB: github.com/mdymczyk/iot-pipeline Our thanks to: LP RESEARCH www.lp-research.com contact: Klaus Peterson klaus@lp-research.com
- 35. © 2017 MapR Technologies 35 Thank you to LP-RESEARCH! Hardware design and production Expertise in Motion sensors Gyroscope Accelerometer Magnetometer Sensor fusion algorithm development Multi-platform application development See all our products: https://www.lp-research.com/products/ LPMS-B2 LPMS-CU2 LPMS-CANAL2 LPMS-USBAL2OEM also available!
Editor's Notes
- Industry 4.0 is all about digitization of the factory. Sensors everywhere. All this data makes possible new opportunities for automation, cost savings, higher productivity and higher quality. What makes a system Industry 4.0 Interoperability — machines, devices, sensors and people that connect and communicate with one another. Information transparency — the systems create a virtual copy of the physical world through sensor data in order to contextualize information. Technical assistance — both the ability of the systems to support humans in making decisions and solving problems andthe ability to assist humans with tasks that are too difficult or unsafe for humans. Decentralized decision-making — the ability of cyber-physical systems to make simple decisions on their own and become as autonomous as possible. Our talk will focus on Data & Analytics for improving the efficiency of operations of factories with lots of industrial robots. We combine Smart sensors, DB Analytics (ML), Cloud computing and AR to power a real-world, state of the art predictive analytics system.
- Order parts predictively Increased factory efficiency Robots operate at peak efficiency
- We have a business goal, a robot and a sensor to work with. We are gonna have to data science the shit out of this [9]https://www.quora.com/Which-type-of-Sensors-use-in-industrial-robots inderesting: no motion sensors! that’s the justification here.
- Based on known real-world requirement of state of the art Japanese car-parts manufacturers. Recall is more important than precision because too many false alarms will increase costs and make trusting the system very hard. Precision can be initially very low and still the system can be useful IF you can trust the predictions. The models can then be improved over time.
- Scale with number of sensors, robots and factories. GB a day quickly become many GB per hour or even minutes. This is comfortably on moderate sized clusters (5-25 nodes) using current big data platforms used by attendees of Strata.
- Working software over complex implementations that never get done
- 異常がない場合、ご覧いただいた通り緑のマークが表示されます。
- 20m mark
- What do we even want?! I.E.: Data gathering Feature selection, extraction, engineering and data transformation 3) Pick all potential algorithms 4) Build a model using your library/tool of choice 5) Evaluate according to previously defined metrics 6) If not good enough then either try a different approach, features or method parameters 7) Otherwise extract the model and put it into production!
- Simplifications: Data is centered around 0 Data is scaled [-1,1] No missing values
- Mention why we are doing it with machine learning at all! No rules, automatically learn the best parameters for each application without new coding and not based on supervised techniques. Especially good when we don’t know what we are looking for: machines can break in a variety of ways.
- Mention why we are doing it with machine learning at all! No rules, automatically learn the best parameters for each application without new coding and not based on supervised techniques. Especially good when we don’t know what we are looking for: machines can break in a variety of ways. Peeking: ML modeling mistake where some data is used to train a model includes information about the answer
- Short mention of what does ”large” reconstruction error mean? Discussion of thresholds and why SD is a good choice.
- MSE measures the average of the squares of the errors or deviations—that is, the difference between the estimator and what is estimated. The MSE is a measure of the quality of an estimator—it is always non-negative, and values closer to zero are better. Note: using RMSE is popular too and is a scaled version of MSE, otherwise it’s identical.
- Circle back to slide
- RNNs can remember their former inputs and operate over a sequence of vectors. Good for time series? Training with Back Propagation Through Time is unstable1 Effective limit of RNNs is 5-10 discreet time steps2 “Works slightly better (than RNN) in practice, owing to its more powerful update equation and some appealing backpropagation dynamics” - Andrej Karpathy I mentioned the remarkable results people are achieving with RNNs. Essentially all of these are achieved using LSTMs. They really work a lot better for most tasks! – C Olah text and speech : (Google Translate, Apple’s Siri, Amazon’s Alexa)
- LSTM Can learn in excess of 1000 discreet time steps Algorithm is local in space and time Computational complexity per time step/weight is O(1) Keras has implementation of LSTM layer Lots of examples are available (TODO: 1, 2, 3)
- Keras and TF stil need expertise unavailable to most engineers, but it’s a huge step in the right direction Prediction throughput is slower, will need more engineering to make it work properly
- Mention Convergence
- Have a clear plan for production Data Science + Data Engineering = Win Effort: Data engineering > data science