FROM ATTENTION WEIGHTS TO ATTENTION ZONES: LOCALIZING FAULT-RELEVANT SIGNALS AND TIME INTERVALS IN MULTIVARIATE TIME-SERIES

Root cause identification from multivariate time-series data in automotive systems is challenging because labels are typically available only at the vehicle level, not for individual signals or time steps, leading to a weakly supervised learning problem. This paper presents an attention-zone identification framework based on a hybrid convolutional–transformer architecture that jointly models local channel-wise patterns and global temporal structure to localize fault-relevant signals and time windows. Channel-wise CNN encoders preserve signal-level interpretability, while a transformer encoder enable attribution of the classification decision to specific channels (“attention channels”) and time intervals (“attention zones”) via attention weights, without post-hoc explanation methods. The method is evaluated on (i) a benchmark Racket Sports dataset augmented with synthetic localized faults and (ii) a large-scale vehicles dataset representing a complex real-world data analysis task. The results demonstrate that the proposed attention-zone framework provides robust, intrinsically interpretable root-cause insights for weakly supervised multivariate time-series diagnostics in complex automotive systems.