Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

Organizations depend on heavy use of various cyber defense technologies, including
intrusion detection and prevention systems, to monitor and protect networks and devices …

多维时间序列上的异常检测, 是时态数据分析的重要研究问题之一. 近年来, 工业互联网中传感器
设备采集并积累了大量工业时间序列数据, 这些数据具有模式多样, 工况多变的特性 …

Currently, data are often referred to as the oil of the 21st century. This comparison is not only
used to express that the resource data are just as important for the fourth industrial …

Data cleaning is one of the most important tasks in data analysis processes. One of the
perennial challenges in data analytics is the detection and handling of non-valid data …

Nowadays, the issue of MV imputation has become one of the research hotspots in the field
of data quality, since the missing values (MVs) are prevalent in real-world datasets and bring …

Intelligent devices often produce time series data that suffer from significant data quality
issues. While the utilization of data dependency in error detection and data repair has been …

The explosive growth of time series captured by sensors in industrial pipelines gives rise to
the flourish of intelligent industry. Exploiting the value of these time series is conductive to …

The increasing interest among manufacturers in monitoring and analyzing industrial systems
is generating a problem related to the considerable costs associated with the storage of the …