{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:03:50Z","timestamp":1771074230704,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,14]],"date-time":"2018-09-14T00:00:00Z","timestamp":1536883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.<\/jats:p>","DOI":"10.3390\/s18093098","type":"journal-article","created":{"date-parts":[[2018,9,14]],"date-time":"2018-09-14T10:57:59Z","timestamp":1536922679000},"page":"3098","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Imaging Time Series for the Classification of EMI Discharge Sources"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9169-835X","authenticated-orcid":false,"given":"Imene","family":"Mitiche","sequence":"first","affiliation":[{"name":"Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gordon","family":"Morison","sequence":"additional","affiliation":[{"name":"Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Nesbitt","sequence":"additional","affiliation":[{"name":"Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Hughes-Narborough","sequence":"additional","affiliation":[{"name":"Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8084-573X","authenticated-orcid":false,"given":"Brian G.","family":"Stewart","sequence":"additional","affiliation":[{"name":"Institute of Energy and Environment, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Boreham","sequence":"additional","affiliation":[{"name":"Innovation Centre for Online Systems, 7 Townsend Business Park, Bere Regis BH20 7LA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/TIA.2016.2603467","article-title":"Condition assessment of electrical apparatus with EMI diagnostics","volume":"53","author":"Timperley","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Song, H., Wu, Q., and Dong, H. (2017, January 20\u201323). Emi-based diagnosis to grounding grids by combining ensemble empirical mode decomposition and ICA. Proceedings of the 8th International Conference on Computer Modeling and Simulation, Canberra, Australia.","DOI":"10.1145\/3036331.3036361"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1080\/09205071.2017.1353925","article-title":"Space-time MUSIC imaging of emi sensing data and a subspace partition study","volume":"31","author":"Song","year":"2017","journal-title":"J. Electromagnet. Wave"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mitiche, I., Morison, G., Nesbitt, A., Hughes-Narborough, M., Stewart, B.G., and Boreham, P. (2018). Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. Sensors, 18.","DOI":"10.3390\/s18020406"},{"key":"ref_5","unstructured":"Wang, Z., and Oates, T. (2015, January 25\u201331). Imaging time-series to improve classification and imputation. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/TSMCC.2011.2118750","article-title":"Local binary patterns and Its application to facial image analysis: a survey","volume":"41","author":"Huang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. C Appl. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"V\u0103t\u0103manu, O.A., and Jivulescu, M. (2013, January 23\u201325). Image classification using local binary pattern operators for static images. Proceedings of the IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI.2013.6608962"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1109\/TIP.2015.2476955","article-title":"Texture classification using local pattern based on vector quantization","volume":"24","author":"Pan","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1016\/j.patrec.2012.07.007","article-title":"Local phase quantization descriptor for improving shape retrieval","volume":"33","author":"Nannia","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ojansivu, V., and Heikkil\u00e4, J. (2008, January 1\u20133). Blur insensitive texture classification using local phase quantization. Proceedings of the 3rd international conference on Image and Signal Processing, Cherbourg-Octeville, France.","DOI":"10.1007\/978-3-540-69905-7_27"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Baek, W., Baek, S., and Kim, D.Y. (2018). Characterization of system status signals for multivariate time series discretization based on frequency and amplitude variation. Sensors, 18.","DOI":"10.3390\/s18010154"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isatra.2015.04.006","article-title":"Separation of sources in radiofrequency measurements of partial discharges using time-power ratios maps","volume":"58","author":"Robles","year":"2015","journal-title":"ISA Trans."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9882","DOI":"10.3390\/s150509882","article-title":"Separation of radio-frequency sources and localization of partial discharges in noisy environments","volume":"5","author":"Robles","year":"2015","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez, F., Garnacho, F., Khamlichi, F., and Ortego, J. (2016, January 3\u20137). Classification of partial discharge sources by the characterization of the pulses waveform. Proceedings of the IEEE International Conference on Dielectrics (ICD), Montpellier, France.","DOI":"10.1109\/ICD.2016.7547655"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/TPWRD.2004.843397","article-title":"Radiometric location of partial discharge sources on energized high-voltage plant","volume":"20","author":"Moore","year":"2005","journal-title":"IEEE Trans. Power Del."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Asiri, Y., Vouk, A., Renforth, L., Clark, D., and NeuralWare, J.C. (2011, January 5\u20138). Neural network based classification of partial discharge in HV motors. Proceedings of the Electrical Insulation Conference (EIC), Annapolis, MD, USA.","DOI":"10.1109\/EIC.2011.5996173"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1002\/tee.22385","article-title":"Classification of multiple partial discharge sources in dielectric insulation material using Cepstrum analysis\u2013artificial neural network","volume":"12","author":"Hazlee","year":"2017","journal-title":"IEEJ T. Electr. Electron. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TDEI.2010.5412014","article-title":"Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses","volume":"17","author":"Dey","year":"2010","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/94.839339","article-title":"Wavelet analysis for classification of multi-source PD patterns","volume":"7","author":"Lalitha","year":"2000","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1016\/j.eswa.2011.08.140","article-title":"Artial discharge pattern recognition of power cable joints using extension method with fractal feature enhancement","volume":"39","author":"Gu","year":"2012","journal-title":"Expert. Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6575","DOI":"10.1016\/j.eswa.2011.12.044","article-title":"Pattern recognition with cerebellar model articulation controller and fractal features on partial discharges","volume":"39","author":"Chen","year":"2012","journal-title":"Expert. Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1049\/iet-gtd.2012.0080","article-title":"Fractal features-based pattern recognition of partial discharge in xlpe power cables using extension method","volume":"6","author":"Chen","year":"2012","journal-title":"IET Gener. Transm. Dis."},{"key":"ref_23","first-page":"351","article-title":"Investigation of Partial discharge on typical defects with uhf detection method for gis","volume":"88","author":"Tang","year":"2012","journal-title":"Electrotech. Rev."},{"key":"ref_24","first-page":"1","article-title":"Classification of partial discharge measured under different levels of noise contamination","volume":"12","author":"Wong","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TDEI.2016.005887","article-title":"Classification of simultaneous multiple partial discharge sources based on probabilistic interpretation using a two-step logistic regression algorithm","volume":"24","author":"Janani","year":"2017","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/61.368411","article-title":"Neural network system using the multi-layer perceptron technique for the recognition of PD pulse shapes due to cavities and electrical trees","volume":"10","author":"Mazroua","year":"1995","journal-title":"IEEE Trans. Power Del."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/94.839349","article-title":"Fuzzy logic applied to PD pattern classification","volume":"7","author":"Salama","year":"2000","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TDEI.2002.1007695","article-title":"Digital detection and fuzzy classification of partial discharge signals","volume":"9","author":"Contin","year":"2002","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TDEI.2013.6678860","article-title":"Autonomous classification of pd sources within three-phase 11 kv pilc cables","volume":"20","author":"Hunter","year":"2013","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1049\/iet-smt.2012.0024","article-title":"Spectral features for the classification of partial discharge signals from selected insulation defect models","volume":"7","author":"Ambikairajah","year":"2013","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Albarrac\u00edn, R., Ardila-Rey, J.A., and Masud, A.A. (2016). On the use of monopole antennas for determining the effect of the enclosure of a power transformer tank in partial discharges electromagnetic propagation. Sensors, 16.","DOI":"10.3390\/s16020148"},{"key":"ref_32","unstructured":"(2015). Specification for Radio Disturbance and Immunity Measuring Apparatus and Methods, International Electronic Commission. CISPR-16-1-1."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Timperley, J.E., Vallejo, J.M., and Nesbitt, A. (2014, January 8\u201311). Trending of EMI data over years and overnight. Proceedings of the IEEE Electrical Insulation Conference, Philadelphia, PA, USA.","DOI":"10.1109\/EIC.2014.6869370"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dengwen, Z. (2010, January 16\u201318). An edge-directed bicubic interpolation algorithm. Proceedings of the 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5647190"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ins.2017.08.059","article-title":"Local phase quantization plus: A principled method for embedding local phase quantization into fisher vector for blurred image recognition","volume":"420","author":"Xiao","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1016\/j.sigpro.2012.04.023","article-title":"Music genre classification using lbp textural features","volume":"92","author":"Costa","year":"2012","journal-title":"Sig. Proc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Battaglino, D., Lepauloux, L., Pilati, L., and Evans, N. (2015, January 18\u201321). Acoustic context recognition using local binary pattern codebooks. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA.","DOI":"10.1109\/WASPAA.2015.7336886"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Topi, M., Timo, O., Matti, P., and Maricor, S. (2000, January 3\u20137). Robust texture classification by subsets of local binary patterns. Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain.","DOI":"10.1109\/ICPR.2000.903698"},{"key":"ref_40","first-page":"1359","article-title":"Shannon, levy, and tsallis: A note","volume":"2","author":"Rathie","year":"2008","journal-title":"Appl. Math. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Criminisi, A., Konukoglu, E., and Shotton, J. (2011). Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-supervised Learning, Microsoft Technical Report.","DOI":"10.1561\/9781601985415"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Y., Chao, H.-C., Zhang, Z., and Zhang, Z. (2018). Classification of incomplete data based on evidence theory and an extreme learning machine in wireless sensor networks. Sensors, 18.","DOI":"10.3390\/s18041046"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, M., Chen, S., Zhao, X., and Yang, Z. (2018). Research on construction workers\u2019 activity recognition based on smartphone. Sensors, 18.","DOI":"10.3390\/s18082667"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Alippi, C., and Roveri, M. (2010, January 18\u201323). Virtual k-fold cross validation: An effective method for accuracy assessment. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596899"},{"key":"ref_45","unstructured":"Vanwinckelen, G., and Blockeel, H. (2012, January 24\u201325). On estimating model accuracy with repeated cross-validation. Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, Ghent, Belgium."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhang, H., Mao, Q., and Li, Z. (2018). Land cover classification with gf-3 polarimetric synthetic aperture radar data by random forest classifier and fast super-pixel segmentation. Sensors, 18.","DOI":"10.3390\/s18072014"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dong, H., Xu, X., Wang, L., and Pu, F. (2018). Gaofen-3 polsar image classification via xgboost and polarimetric spatial information. Sensors, 18.","DOI":"10.3390\/s18020611"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Su, J., Yi, D., Liu, C., Guo, L., and Chen, W.-H. (2018). Dimension reduction aided hyperspectral image classification with a small-sized training dataset: experimental comparisons. Sensors, 18.","DOI":"10.3390\/s17122726"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Oh, S.-I., and Kang, H.-B. (2017). Object detection and classification by decision-level fusion for intelligent vehicle systems. Sensors, 17.","DOI":"10.3390\/s17010207"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cao, F., Yang, Z., Ren, J., Jiang, M., and Ling, W.-K. (2017). Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors, 17.","DOI":"10.3390\/s17112603"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bystrov, A., Hoare, E., Tran, T.-Y., Clarke, N., Gashinova, M., and Cherniakov, M. (2017). Automotive system for remote surface classification. Sensors, 17.","DOI":"10.3390\/s17040745"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/3098\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:20:35Z","timestamp":1760196035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/3098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,14]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["s18093098"],"URL":"https:\/\/doi.org\/10.3390\/s18093098","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,14]]}}}