Masanao Natsumeda NEC Labs America

Masanao Natsumeda

Research Staff (Tokyo)

Data Science and System Security


Unsupervised Anomaly Detection Under A Multiple Modeling Strategy Via Model Set Optimization Through Transfer Learning

Unsupervised anomaly detection approaches have been widely accepted in applications for industrial systems. Industrial systems often operate with multiple modes since they work for multiple purposes or under different conditions. In order to deal with the difficulty of anomaly detection due to multiple operating modes, multiple modeling strategies are employed. However, estimating the optimal set of models is a challenging problem due to the lack of supervision and computational burden. In this paper, we propose DeconAnomaly, a deep learning framework to estimate the optimal set of models using transfer learning for unsupervised anomaly detection under a multiple modeling strategy. It reduces computational burden with transfer learning and optimizes the number of models based on a surrogate metric of detection performance. The experimental results show clear advantages of DeconAnomaly.

Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection

Prognostics or early detection of incipient faults by leveraging the monitoring time series data in complex systems is valuable to automatic system management and predictive maintenance. However, this task is challenging. First, learning the multi-dimensional heterogeneous time series data with various anomaly types is hard. Second, the precise annotation of anomaly incipient periods is lacking. Third, the interpretable tools to diagnose the precursor symptoms are lacking. Despite some recent progresses, few of the existing approaches can jointly resolve these challenges. In this paper, we propose MCDA, a deep multi-instance contrastive learning approach with dual attention, to detect anomaly precursor. MCDA utilizes multi-instance learning to model the uncertainty of precursor period and employs recurrent neural network with tensorized hidden states to extract precursor features encoded in temporal dynamics as well as the correlations between different pairs of time series. A dual attention mechanism on both temporal aspect and time series variables is developed to pinpoint the time period and the sensors the precursor symptoms are involved in. A contrastive loss is designed to address the issue that annotated anomalies are few. To the best of our knowledge, MCDA is the first method studying the problem of ‘when’ and ‘where’ for the anomaly precursor detection simultaneously. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness of MCDA.

RULENet: End-to-end Learning with the Dual-estimator for Remaining Useful Life Estimation

Remaining Useful Life (RUL) estimation is a key element in Predictive maintenance. System agnostic approaches which just utilize sensor and operational time series have gained popularity due to its ease of implementation. Due to the nature of measurement or degradation mechanisms, its accurate estimation is not always feasible. Existing methods suppose the range of RUL with feasible estimation is given from results at upstream tasks or prior knowledge. In this work, we propose the novel framework of end-to-end learning for RUL estimation, which is called RULENet. RULENet simultaneously optimizes its Dual-estimator for RUL estimation and its feasible range estimation. Experimental results on NASA C-MAPSS benchmark data show the superiority of the end-to-end framework.