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Benchmark Datasets for Fault Detection and Classification in Sensor Data

Bas de Bruijn, Tuan Anh Nguyen, Doina Bucur, Kenji Tei
2016 Proceedings of the 5th International Confererence on Sensor Networks  
On the other hand, the applications will expect accurate sensor data, and recent literature proposes algorithmic solutions for the fault detection and classification in sensor data.  ...  We present algorithmic procedures and a software tool for preparing further such benchmark datasets.  ...  Smart Offices project, contract no. 647.000.004 and 3) the Dutch National Research Council Beijing Groningen Smart Energy Cities project, contract no. 467-14-037.  ... 
doi:10.5220/0005637901850195 dblp:conf/sensornets/BruijnNBT16 fatcat:hndmc4343zcwpalbsgfhwaqbg4

A heterogeneous benchmark dataset for data analytics: Multiphase flow facility case study

Anna Stief, Ruomu Tan, Yi Cao, James R. Ottewill, Nina F. Thornhill, Jerzy Baranowski
2019 Journal of Process Control  
The presented dataset is suitable for developing and validating algorithms for fault detection and diagnosis and data fusion concepts.  ...  Data may take a number of forms, from time-domain signals, sampled at various rates using a variety of sensors, to alarm and event logs.  ...  of the studies presented in this paper.  ... 
doi:10.1016/j.jprocont.2019.04.009 fatcat:hlbrxjkgpbd5vj7ivqvfzqn6fq

Analytics Of Heterogeneous Process Data: Multiphase Flow Facility Case Study

Anna Stief, Ruomu Tan, Yi Cao, James R. Ottewill
2018 Zenodo  
This benchmark case study with data from disparate sources can be used for algorithm development and validation for fault detection, fault identification, fault classification, fault severity detection  ...  Two different fault detection algorithms are applied to the data, a multivariate PCA-enhanced Canonical Variate Analysis (CVA) and a probabilistic Bayesian method.  ...  The authors are thankful for Liyun Lao, Stan Collins, Henry Tandoh, Prafull Sharma and Godfrey Nnabuife for providing helpful insight to the design and the implementation of the experiment.  ... 
doi:10.5281/zenodo.1405589 fatcat:zv7jk47jlrb57ccbzwyzdku3oq

Fault detection and classification in Industrial IoT in case of missing sensor data

Merim Dzaferagic, Nicola Marchetti, Irene Macaluso
2021 IEEE Internet of Things Journal  
Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct  ...  on the fault detection and classification modules.  ...  system, i.e. fault detection, fault classification, and data imputation.  ... 
doi:10.1109/jiot.2021.3116785 fatcat:yvhsuzkacfhl3pnu6g3mkpqfou

Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

Yichuan Fu, Zhiwei Gao, Yuanhong Liu, Aihua Zhang, Xiuxia Yin
2020 Processes  
for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems.  ...  In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios.  ...  Acknowledgments: The authors would like to thank the research support from the E & E faculty at University of Northumbria (UK), the National Nature Science Foundation of China (NNSFC) under grant 61673074, and  ... 
doi:10.3390/pr8091066 fatcat:ho5t3l555fey7nwpahansmthjm

LeakDB : A benchmark dataset for leakage diagnosis in water distribution networks

Stelios G. Vrachimis, Marios S. Kyriakou, Demetrios G. Eliades, Marios M. Polycarpou
2018 Zenodo  
The increase of streaming data from water utilities is enabling the development of real-time anomaly and fault detection algorithms that can detect events, such as pipe bursts and leakages.  ...  The dataset is stored on an open research data repository, and will be updated in the future with new simulation scenarios.  ...  Future work on the benchmark can include additional features such as: 1) considering various anomalies that are due to sensor measurements such as spikes in data, missing measurements, sensor faults and  ... 
doi:10.5281/zenodo.1313116 fatcat:tc65pcnhpncmrd3ss6gibj3fyi

A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Hong Wang, Hongbin Wang, Guoqian Jiang, Yueling Wang, Shuang Ren
2020 Sensors  
This paper proposes a novel classification-based fault detection method for wind turbine sensors.  ...  Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines.  ...  This paper proposes a novel classification-based fault detection method for wind turbine sensors.  ... 
doi:10.3390/s20123580 pmid:32599907 pmcid:PMC7349861 fatcat:qwq6gisjrzfirmeeggr2eovih4

QoS-Aware Fault Detection in Wireless Sensor Networks

Alessandra De Paola, Giuseppe Lo Re, Fabrizio Milazzo, Marco Ortolani
2013 International Journal of Distributed Sensor Networks  
This paper proposes a fully distributed algorithm for detecting data faults, taking into account the response time besides the classification accuracy.  ...  This issue is usually addressed through "fault detection" algorithms that classify readings by exploiting temporal and spatial correlations.  ...  On the contrary, early detection of data faults helps reducing the amount of sensory data to be processed by high-level systems, and in-network fault detection is in fact a crucial functionality for many  ... 
doi:10.1155/2013/165732 fatcat:42urdivgmbfjlitqd27ar6j63y

Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning

Jinseok Kim, Ki-Il Kim
2021 Sustainability  
Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years.  ...  Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and cables.  ...  Data Availability Statement: Data available on request. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su13094692 doaj:3fa9250f5c8848fa8685f71512a695d7 fatcat:v2pdz3sx4rbanjjvuvt6uhpbaa

Online Fault Classification in HPC Systems through Machine Learning [article]

Alessio Netti, Zeynep Kiziltan, Ozalp Babaoglu, Alina Sirbu, Andrea Bartolini, Andrea Borghesi
2019 arXiv   pre-print
For this reason, detecting and classifying faults in HPC systems as they occur and initiating corrective actions before they can transform into failures will be essential for continued operation.  ...  In this paper, we propose a fault classification method for HPC systems based on machine learning that has been designed specifically to operate with live streamed data.  ...  Sîrbu has been partially funded by the EU project SoBigData Research Infrastructure -Big Data and Social Mining Ecosystem (grant agreement 654024).  ... 
arXiv:1810.11208v2 fatcat:goopxt2k2ja63hzc33jqyq4iqi

Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving [article]

Błażej Leporowski, Daniella Tola, Casper Hansen, Alexandros Iosifidis
2021 arXiv   pre-print
In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a  ...  Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process.  ...  Experiments In this Section, we provide a benchmark of the use of AURSAD dataset for multi-class classification and binary anomaly detection problems.  ... 
arXiv:2107.01955v1 fatcat:ditzbj272vgaxcbqsunqk67fq4

Data Repository for Sensor Network : A Data Mining Approach

Srinivas Narasegouda
2014 International Journal of Database Management Systems  
Hence, in this paper we are proposing to use the combination of quantitative association rules and decision tree for classification of faulty data and normal data.  ...  The development of sensor data repositories will aid the researchers to create benchmark dataset.  ...  Fuzzy data fusion mechanism [6] and fuzzy classifier mechanism [7] were also developed for fault detection in sensor network.  ... 
doi:10.5121/ijdms.2014.6401 fatcat:m2hp6fwmy5cajhym3mctovv6j4

Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets

Emmanuel Ramasso, Abhinav Saxena
2020 International Journal of Prognostics and Health Management  
However, in the absence of performance benchmarking results and due to common misunderstandings in interpreting the relationships between these datasets, it has been difficult for the users to suitably  ...  In addition to identifying differentiating characteristics in these datasets, this paper also provides performance results for the PHM'08 data challenge wining entries to serve as performance baseline.  ...  11-LABX-01-01) and with partial support from NASA's System-wide Safety and Assurance Technologies (SSAT) Project under ARMD/Aviation Safety program.  ... 
doi:10.36001/ijphm.2014.v5i2.2236 fatcat:sibalulnujafxnehycitmimubm

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Pangun Park, Piergiuseppe Di Marco, Hyejeon Shin, Junseong Bang
2019 Sensors  
In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data.  ...  It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis.  ...  In Section 5, we present temporal fault detection and fault diagnosis results on the benchmark dataset of the proposed technique. Section 6 summarizes this paper.  ... 
doi:10.3390/s19214612 pmid:31652821 pmcid:PMC6866134 fatcat:mboe65xzkbakhgl5cp43folivq

Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs

Tuan Anh Nguyen, Doina Bucur, Marco Aiello, Kenji Tei
2013 Proceedings of the Fourth Symposium on Information and Communication Technology - SoICT '13  
We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection.  ...  A combination of neighbourhood voting and time series data analysis techniques are used to detect faults.  ...  Tuan Anh Nguyen thanks the Honiden lab at the National Institute of Informatics, Tokyo, Japan for supporting him during his internship during which he conducted the reported research.  ... 
doi:10.1145/2542050.2542080 dblp:conf/soict/NguyenBAT13 fatcat:qxxqxgdxk5bfjhbnsma7yqor2e
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