Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Hierarchical non-linear factor analysis and topographic maps
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A comparison of DFT and DWT based similarity search in time-series databases
Proceedings of the ninth international conference on Information and knowledge management
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Clustering of time series data-a survey
Pattern Recognition
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Feature extraction using a fast null space based linear discriminant analysis algorithm
Information Sciences: an International Journal
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis
Information Sciences: an International Journal
A time series forest for classification and feature extraction
Information Sciences: an International Journal
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Rapidly increasing complexity of vehicle systems is calling for technologies to promptly analyze field problems, and effectively identify weakness in vehicle engineering design, in order to enhance product quality. Recent vehicular communication technologies allow for remote access to extensive amount of vehicle data in a cost-effective way, which enables in-depth field issue analysis. However, practical solutions are still lacking to effectively turn the massive amount of raw data into actionable design enhancement suggestions. In this paper, we propose a general framework, named Automatic Field Data Analyzer (AFDA), and related algorithms that analyze large volumes of field data, and identify root causes of faults by systematically making use of signal processing, machine learning, and statistical analysis approaches. AFDA evaluates vehicle system performance, generates feature vectors that represent different root causes of faults, and identifies the features that are most relevant to system performance fluctuation, which eventually reveals the underlying reasons for the faults. This paper presents a case study of AFDA in the application of vehicle battery, where gigabytes of real vehicle data are sifted through, and the root causes of field issues are identified. The results well match the findings from experts with years of experiences. The proposed data-based scheme and approaches can be generally applied to any vehicle systems.