Research Article
A Robust Hidden Semi-Markov Model for Anomaly Detection of Centrifugal Compressors Monitoring Data with Missing Values
Issue:
Volume 12, Issue 2, June 2026
Pages:
24-34
Received:
25 June 2026
Accepted:
8 July 2026
Published:
24 July 2026
DOI:
10.11648/j.ijdst.20261202.11
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Abstract: Centrifugal compressors are critical rotating machines in petrochemical and energy systems, and abnormal operating states may lead to unplanned shutdowns, efficiency loss, and safety risks. In practical monitoring systems, sensor data often contain missing samples, local disturbances, and sustained anomalous segments, making conventional pointwise and interpolation-dependent anomaly detectors less reliable. To address these issues, this paper presents a robust incomplete-data hidden semi-Markov model (RID-HSMM) for anomaly detection in monitoring data from centrifugal compressors. The method constructs a two-dimensional observation vector from each observed value and its adjacent first-order difference to represent both amplitude information and local dynamic variation. To avoid treating interpolated values as real observations, the emission likelihood is only evaluated over the available observed feature dimensions. A Student’s t distribution is used as the emission model to improve robustness against heavy-tailed disturbances and local outliers. In the anomaly detection phase, the anomaly score combines the negative log predictive density of the observations and the state-duration deviation, thereby capturing both observation abnormality and state-persistence abnormality. Experiments on real motor-bearing temperature data from a centrifugal compressor were conducted under multiple missingness and anomaly settings. Compared with ARIMA, Matrix Profile, LSTM-AE, USAD, and the robust median baselines, RID-HSMM achieved the highest point-level precision and the lowest false-alarm rate, while maintaining competitive segment-level detection performance. The results indicate that explicit state-duration modeling and pseudo-observation avoidance can improve the reliability of anomaly detection for incomplete industrial time series.
Abstract: Centrifugal compressors are critical rotating machines in petrochemical and energy systems, and abnormal operating states may lead to unplanned shutdowns, efficiency loss, and safety risks. In practical monitoring systems, sensor data often contain missing samples, local disturbances, and sustained anomalous segments, making conventional pointwise ...
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,
Enyun Liang,
Shaolin Hu*
