Numerous industrial environments and IoT systems in the real world contain a range of
sensor devices. These devices, when in operation, produce a large amount of multivariate …

Condition monitoring (CM) technology based on supervisory control and data acquisition
(SCADA) data is crucial for ensuring reliable operation and reducing maintenance costs of …

Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of
the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an …

Minimizing the tardiness of a parallel machine scheduling problem has been actively
studied in modern manufacturing systems. In particular, dynamic parallel machine …

In general, training fault samples of aero-engines are very rare and only collected under one
or a few operating conditions. However, due to diverse operating conditions and fault …

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling
methods and have been widely applied to industrial process monitoring. However, they both …

Time-series DBMSs based on the LSM-tree have been widely applied in numerous
scenarios ranging from daily life to industrial production. Compared to the traditional key …

Directly mining high-dimensional time series presents several challenges, such as time and
space costs. This study proposes a new approach for representing time series data and …

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-
variate time series data, which are of major significance for today's industrial applications …

Modern critical infrastructure such as an electric grid, uses information and communication
technologies (ICT) to increase system performance. However, the integration of Distributed …