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
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 ...
Show More
Research Article
Consumer Privacy, Ethics and Autonomy in a Digital Society
Evans Achara*
Issue:
Volume 12, Issue 2, June 2026
Pages:
35-52
Received:
7 March 2026
Accepted:
20 March 2026
Published:
11 August 2026
DOI:
10.11648/j.ijdst.20261202.12
Downloads:
Views:
Abstract: As enterprises continuously rely on data to effectively drive and power business processes in a digital economy, privacy concerns have emerged as a major source of deep concern among consumers and privacy advocates in a digital society. Several scholars have shared their opinions and perspectives in related articles on this issue since privacy is a fundamental and constitutional right in many countries that must be protected at all times. While several practitioners and industry experts have proffered various privacy-preserving measures to help empower users to make informed decisions that relate to the use of their personal information, others have proposed various privacy preserving measures and mechanisms to help protect the use of personal information in a digital society to create the necessary confidence and trust amongst members of the public. With the current advances in artificial intelligence, social media platforms, and automation, the issue has emerged as a major source of concern among policymakers, privacy advocates, and industry experts. The purpose of the e-Delphi study was to gain consensus from the opinions of industry experts on best practice measures and various effective privacy-preserving measures to help enhance users' privacy and allay privacy concerns in a digital society. The study adopted the Restricted Access/Limited Control (RALC) theory of privacy to provide a theoretical framework for the research study. The study included three rounds of questioning using the Delphi method. The findings from the study revealed that effective privacy-preserving measures, such as Data minimization, Privacy-By-Design, Privacy Labels and icons, Data Ownership and control, Third-party App Permission, Mandatory Data/Privacy Breach Notice, Frequent Policy Updates, End-to-End Encryption, User-Friendly Privacy Control features, and Informed Consent, provided an effective way to allay the fears of consumers in a digital age.
Abstract: As enterprises continuously rely on data to effectively drive and power business processes in a digital economy, privacy concerns have emerged as a major source of deep concern among consumers and privacy advocates in a digital society. Several scholars have shared their opinions and perspectives in related articles on this issue since privacy is a...
Show More
