Applying Machine Learning Algorithms to Support Root Cause Analysis An Experimental Study in Automotive Engineering
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1 Applying Machine Learning Algorithms to Support Root Cause Analysis An Experimental Study in Automotive Engineering Duc Tien, Vu , Munich Advisor: Martin Kleehaus (TUM) Dr. Valentin Solotych (IAV) Chair of Software Engineering for Business Information Systems (sebis) Faculty of Informatics Technische Universität München wwwmatthes.in.tum.de
2 IAV Your Strong Engineering Partner Germany London Stockholm Palo Alto Detroit Paris Modena Moscow / Kaluga Beijing Shanghai Seoul Tokyo Mexico City Pune São Paulo More than 30 sites worldwide More than 30 years of experience More than 6,500 members of staff More than 68 % engineers More than 750 annual turnover ( m) IAV 04/2017 C-CM1 SK1 Status: released, externaly 2
3 Agenda 1. A little start 2. Motivation 3. Research question 4. Assessment of Anomaly Detection Techniques 5. Proposed Technique For a Recommender System 6. Implementation & Evaluation 7. Summary Kick-Off Presentation - Tien Vu sebis 3
4 A little start Data from a failed emission test Component A Is A the root-cause? Emission outcome Is B the root-cause? Component B Kick-Off Presentation - Tien Vu sebis 4
5 A little start Add more data from passed emission test We can tell that B influenced the emission outcome more than A Component A CO2 - Emission Emission outcome Component B Kick-Off Presentation - Tien Vu sebis 5
6 Motivation 1 Engineer must manually check all time series data ( time series) to find out which one is responsible for the anomaly Requires a lot of expert knowledge and time consuming Speed up the process using machine learning to detect anomaly Kick-Off Presentation - Tien Vu sebis 6
7 Motivation 2 Currently: Fixed Driving Cycle (NEDC, WLTC, ) Future: No Fixed Driving Cycle more, that means the car will be driven and measured in real life situation Support from machine learning techniques is required Kick-Off Presentation - Tien Vu sebis 7
8 Research questions What are the related types of time series and anomaly? Which algorithm can be used to detect anomalies in time series data? How can we establish a correlation between time series In which way can the root-cause analysis be supported? Kick-Off Presentation - Tien Vu sebis 8
9 Assessment of Anomaly Detection Techniques 1. Probability-based methods 2. Distance-based methods kneighbors 3. Clustering-based methods 4. Reconstruction-based methods 5. Domain-based methods OneClassSVM 6. Isolation-based methods IsolationForest 7. Information-theory based methods Kick-Off Presentation - Tien Vu sebis 9
10 Assessment of Anomaly Detection Techniques Accuracy test: Accuracy 73,28% 92,67% 81,03% Kick-Off Presentation - Tien Vu sebis 10
11 Assessment of Anomaly Detection Techniques Speed test: Kick-Off Presentation - Tien Vu sebis 11
12 Proposed Technique for A Recommender System Eliminate trivial error Create ranking list based on anomaly percentage Create ranking list based on correlation Combine ranking lists Kick-Off Presentation - Tien Vu sebis 12
13 Elimination of Trivial Error (1) Trivial error : error that can be easily excluded from the test data Example: Impossible value when there is a pre-defined boundary Kick-Off Presentation - Tien Vu sebis 13
14 Elimination of Trivial Error (2) Exclude trivial error without pre-defined boundary Assuming that if a point anomaly is responsible for the high emission outcome, the peak of the emission outcome should be near that time point Kick-Off Presentation - Tien Vu sebis 14
15 Ranking Based on Anomaly Percentage Anomaly percentage = nadp nadp+nndp nadp : Number of data points classified as anomalous nndp : Number of data points classified as normal Kick-Off Presentation - Tien Vu sebis 15
16 Ranking Based on Correlation (1) Finding the signal that mostly related to the change in emission outcome Kick-Off Presentation - Tien Vu sebis 16
17 Ranking Based on Correlation (2) Finding the signal that mostly related to the change in emission outcome Kick-Off Presentation - Tien Vu sebis 17
18 Ranking Based on Correlation (3) Pearson r: Value from -1 bis 1 r=-1 Total negative correlation r=+1 Total positive correlation r=0 No correlation Kick-Off Presentation - Tien Vu sebis 18
19 Combine of Rankings Signal Ranking_ Anomaly Ranking_ Correlation Average A B C D E F G New ranking Kick-Off Presentation - Tien Vu sebis 19
20 Implementation Technology Stack Kick-Off Presentation - Tien Vu sebis 20
21 Process of the Recommender System Training Data Upload MDF Splitting MDF Detect Outlier and Suggest of Elimination Different ML Algorithm Aggregate and Display the Results MDF Outlier OCSVM knn IForest Ranking Kick-Off Presentation - Tien Vu sebis 21
22 Prototype of Recommendation Ranking Kick-Off Presentation - Tien Vu sebis 22
23 Evaluation Experiment setting: 7 problem sets Each problem set is one failed emission test The searched signal (the root-cause) for all problem sets is unknown Task: Using the proposed algorithm to generate a ranking for each problem set. The engineer should confirm whether the searched signal is in top 20 or not Result: In one problem set, the searched signal is in top 20 Kick-Off Presentation - Tien Vu sebis 23
24 Evaluation Explanation for the failure of some problem sets: The searched signal doesn t have the characteristics as expected Kick-Off Presentation - Tien Vu sebis 24
25 Summary Conclusion The algorithm helped pushing the root-cause to the top correctly in one case where the searched signal directly affect the emission outcome If the searched signal indirectly affected the emission outcome, the algorithm can not find it Limitation The algorithm is highly adapted to one type of problem Pearson is very sensitive to time shift Signals related to the searched signal weren t checked Future work Another algorithm using the proposed process for recommender system Kick-Off Presentation - Tien Vu sebis 25
26 Duc Tien Vu Technische Universität München Faculty of Informatics Chair of Software Engineering for Business Information Systems Boltzmannstraße Garching bei München Tel tien.vu@tum.de wwwmatthes.in.tum.de
27 Backup slides Kick-Off Presentation - Tien Vu sebis 27
28 Assessment of Anomaly Detection Techniques Kick-Off Presentation - Tien Vu sebis 28
29 The Data Model Kick-Off Presentation - Tien Vu sebis 29
30 IsolationForest Building a forest of random split a collection of binary tree The root of each tree in the forest is the original data set Each tree will choose randomly a feature of the data set and split it at a random point, divides the data set in two new data set The anomaly score of each data point is calculated by taking average of the length of each path from root to that point in all trees. Data points with lower score than normal are the anomalies. Kick-Off Presentation - Tien Vu sebis 30
31 OneClassSVM Create a hyperplane that separate training data point from the rest of space The hyperplane form a region from the training data point where all data points outside of that region is defined as anomaly Kick-Off Presentation - Tien Vu sebis 31
32 kneighbors For each test data point: Get k closest neighbors Find maximum distance between those k neighbors d_max Find minimum distance between test data point and the k neighbors d_test Test: If d_test<d_max -> Normal data point Else : Anomaly Kick-Off Presentation - Tien Vu sebis 32
33 Challenges Long training /evaluating time Depend on algorithm, 1-9 seconds / Signal ~ 900 Signals Data contains noise The tests and signals have different resolution (1 millisecond 1 second) Limited number from data sets because each emission test is very costly Kick-Off Presentation - Tien Vu sebis 33
34 Challenges Traditional time series analysis is only used for curve fitting / prediction problem Different types of time series require different algorithm It s hard to correctly aggregate results from different algorithm??? Kick-Off Presentation - Tien Vu sebis 34
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