Next Article in Journal
Investigation of the Effect of Physical and Optical Factors on the Optical Performance of a Parabolic Trough Collector
Next Article in Special Issue
Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant
Previous Article in Journal
Modeling and Control of Fluid Flow Networks with Application to a Nuclear-Solar Hybrid Plant
Previous Article in Special Issue
Approximate Analysis of Multi-State Weighted k-Out-of-n Systems Applied to Transmission Lines
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1913;

Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation

State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
This paper is an extended version of our paper published in Guo, C., Li, G., Zhang, H., Ju, X., Zhang, Y., Wang, X. Defect Distribution Prognosis of High Voltage Circuit Breakers with Enhanced Latent Dirichlet Allocation. In Proceedings of the International Conference on Prognostics and Health Management (PHM-Harbin 2017), Harbin, China, 9–12 July 2017.
Authors to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 11 November 2017 / Accepted: 15 November 2017 / Published: 20 November 2017
(This article belongs to the Special Issue 2017 Prognostics and System Health Management Conference)
Full-Text   |   PDF [4441 KB, uploaded 21 November 2017]   |  


The continual accumulation of power grid failure logs provides a valuable but rarely used source for data mining. Sequential analysis, aiming at exploiting the temporal evolution and exploring the future trend in power grid failures, is an increasingly promising alternative for predictive scheduling and decision-making. In this paper, a temporal Latent Dirichlet Allocation (TLDA) framework is proposed to proactively reduce the cardinality of the event categories and estimate the future failure distributions by automatically uncovering the hidden patterns. The aim was to model the failure sequence as a mixture of several failure patterns, each of which was characterized by an infinite mixture of failures with certain probabilities. This state space dependency was captured by a hierarchical Bayesian framework. The model was temporally extended by establishing the long-term dependency with new co-occurrence patterns. Evaluation of the high voltage circuit breakers (HVCBs) demonstrated that the TLDA model had higher fidelities of 51.13%, 73.86%, and 92.93% in the Top-1, Top-5, and Top-10 failure prediction tasks over the baselines, respectively. In addition to the quantitative results, we showed that the TLDA can be successfully used for extracting the time-varying failure patterns and capture the failure association with a cluster coalition method. View Full-Text
Keywords: failure prognosis; Latent Dirichlet Allocation; high voltage circuit breakers failure prognosis; Latent Dirichlet Allocation; high voltage circuit breakers

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, G.; Wang, X.; Yang, A.; Rong, M.; Yang, K. Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation. Energies 2017, 10, 1913.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top