Hierarchical symbolic dynamic filtering of streaming non-stationary time series data

Akintayo, Adedotun; Sarkar, Soumik

doi:10.1016/j.sigpro.2018.04.025

Cited by 6 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more detailed derivation, please refer to [39,44]. Note that, joint state sequences Φ M and Φ T can also be applied to compute the conditional probability.…”

Section: Inputs Out-of-sample Disaggregationmentioning

confidence: 99%

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

et al. 2018

Self Cite

View full text Add to dashboard Cite

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

show abstract

“…For more detailed derivation, please refer to [39,44]. Note that, joint state sequences Φ M and Φ T can also be applied to compute the conditional probability.…”

Section: Inputs Out-of-sample Disaggregationmentioning

confidence: 99%

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…[11,12] projected unsupervised spatiotemporal graphical modelling approach to detect anomalies in distributed cyber-physical systems and energy prediction of complex dynamical systems. Hierarchical method was used to extract features of time-scale non-stationary data and found the developed algorithm enables unsupervised data analysis [13]. In the work of [14], the authors developed a comprehensive methodology for detection and integrated diagnostics of gas turbine sensors.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of steam turbine with hierarchical pre‐warning strategy

Yao

Fan

Wang

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

Anomaly detection of steam turbines is to recognize infrequent instances within sensor data that plays a vital role in stable power supply. Machine learning models have been applied to diagnose the faults of turbine and verified useful for identifying engine problem. To detect anomalies of steam turbines with machine learning methods, here, an approach called hierarchical pre-warning strategy is proposed that combines clustering methods with classification methods. Three different clustering methods, K-means, Isolation Forest and Local Outlier Factor, are chosen to separate anomalies from normal data. Since clustering results cannot give unanimous decision, the clustering instances are labelled with three classes, real anomalies, suspected anomalies and normal data, according to their overlapping recognition. Subsequently, five classification algorithms, k-nearest neighbour, support vector machine, decision tree, random forest and gradient boosting decision tree, have been examined to train the labelled data set. The classification results illustrate that gradient boosting decision tree and random forest are much more precise to detect real anomalies of steam turbines. The real anomalies identified by clustering methods have been classified into suspected anomalies by this approach that is more practicable and consistent with ground truth.

show abstract