TY - GEN
T1 - Anomaly Detection in Mixed Time-Series Using A Convolutional Sparse Representation with Application to Spacecraft Health Monitoring
AU - Pilastre, Barbara
AU - Silva, Gustavo
AU - Boussouf, Loic
AU - D'Escrivan, Stephane
AU - Rodriguez, Paul
AU - Tourneret, Jean Yves
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and crucially, it encodes each input signal (either continuous or discrete) from a joint activation and uniform combinations of filters, allowing the correlation across the input signals to be captured. The performance of C-ADDICT, is evaluated on a representative dataset composed of real spacecraft telemetries with an available ground-truth, providing promising results.
AB - This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and crucially, it encodes each input signal (either continuous or discrete) from a joint activation and uniform combinations of filters, allowing the correlation across the input signals to be captured. The performance of C-ADDICT, is evaluated on a representative dataset composed of real spacecraft telemetries with an available ground-truth, providing promising results.
KW - Anomaly detection
KW - convolutional sparse representation
KW - dictionary learning
KW - shift-invariant
UR - http://www.scopus.com/inward/record.url?scp=85089208847&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9053929
DO - 10.1109/ICASSP40776.2020.9053929
M3 - Conference contribution
AN - SCOPUS:85089208847
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3242
EP - 3246
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -