Multi-label Prediction in Time Series Data using Deep Neural Networks

Wenyu Zhang, Devesh K. Jha, Emil Laftchiev, and Daniel Nikovski
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Publication Issue: 
Special Issue on Deep Learning and Emerging Analytics
Submission Type: 
Full Paper
ijphm_19_026.pdf1.03 MBDecember 30, 2019 - 1:51pm

This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While fault (event) detection problems have been thoroughly studied in literature, most of the state-of-the-art techniques can't reliably predict faults (events) over a desired future horizon. In the most general setting of these types of problems, one or more samples of data across multiple time series can be assigned several concurrent fault labels from a finite, known set and the task is to predict the possibility of fault occurrence over a desired time horizon. This type of problem is usually accompanied by strong class imbalances where some classes are represented by only a few samples. Importantly, in many applications of the problem such as fault prediction and predictive maintenance, it is exactly these rare classes that are of most interest. To address the problem, this paper proposes a general approach that utilizes a multi-label recurrent neural network with a new cost function that accentuates learning in the imbalanced classes. The proposed algorithm is tested on two public benchmark datasets: an industrial plant dataset from the PHM Society Data Challenge, and a human activity recognition dataset. The results are compared with state-of-the-art techniques for time-series classification and evaluation is performed using the F1-score, precision and recall.

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Submission Keywords: 
Anomaly Prediction
Anomaly Localization
Anomaly Diagnosis
Condition Monitoring; Prognostics; Deep Learning; Time Series Embeddings; Recurrent Neural Networks
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
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