Predictive maintenance in the automotive sector: A literature review
With the rapid advancement of sensor and network technology, there has been a notable
increase in the availability of condition-monitoring data such as vibration, temperature …
increase in the availability of condition-monitoring data such as vibration, temperature …
Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …
Deep learning fault diagnosis method based on global optimization GAN for unbalanced data
Deep learning can be applied to the field of fault diagnosis for its powerful feature
representation capabilities. When a certain class fault samples available are very limited, it …
representation capabilities. When a certain class fault samples available are very limited, it …
Clustering-based anomaly detection in multivariate time series data
Multivariate time series data come as a collection of time series describing different aspects
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
Relapse prediction in schizophrenia through digital phenotyping: a pilot study
Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of
those discharged may relapse within 1 year even with appropriate treatment. Passively …
those discharged may relapse within 1 year even with appropriate treatment. Passively …
Satellite telemetry data anomaly detection using causal network and feature-attention-based LSTM
Z Zeng, G Jin, C Xu, S Chen, Z Zeng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Most of the data-driven satellite telemetry data anomaly detection methods suffer from high
false positive rate (FPR) and poor interpretability. To solve the above problems, we propose …
false positive rate (FPR) and poor interpretability. To solve the above problems, we propose …
Multivariate time series anomaly detection: A framework of Hidden Markov Models
In this study, we develop an approach to multivariate time series anomaly detection focused
on the transformation of multivariate time series to univariate time series. Several …
on the transformation of multivariate time series to univariate time series. Several …
Constructing robust health indicators from complex engineered systems via anticausal learning
In prognostics and health management (PHM), the task of constructing comprehensive
health indicators (HI) from huge amounts of condition monitoring data plays a crucial role …
health indicators (HI) from huge amounts of condition monitoring data plays a crucial role …
Detecting anomalous energy consumption using contextual analysis of smart meter data
Energy consumption is dependent on temperature, humidity, occupancy, occupant type,
building area etc. All these factors collectively define the context of an energy meter. Once …
building area etc. All these factors collectively define the context of an energy meter. Once …
An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series
A multivariate time series forecasting is critical in many applications, such as signal
processing, finance, air quality forecasting, and pattern recognition. In particular …
processing, finance, air quality forecasting, and pattern recognition. In particular …