{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T02:53:50Z","timestamp":1764557630981,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019629","name":"Universidad de Burgos","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019629","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies\/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.<\/jats:p>","DOI":"10.1007\/s10044-021-01053-0","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T13:09:51Z","timestamp":1643029791000},"page":"271-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A visual tool for monitoring and detecting anomalies in robot performance"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7289-4689","authenticated-orcid":false,"given":"Nu\u00f1o","family":"Basurto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5567-9194","authenticated-orcid":false,"given":"Carlos","family":"Cambra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2444-5384","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Herrero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"unstructured":"Commission E (2014) Study on cross-cutting KETs (Ro-cKETs) . https:\/\/ec.europa.eu\/growth\/industry\/policy\/key-enabling-technologies\/eu-actions\/ro-ckets_en","key":"1053_CR1"},{"key":"1053_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.robot.2017.06.002","volume":"97","author":"B Khaldi","year":"2017","unstructured":"Khaldi B, Harrou F, Cherif F, Sun Y (2017) Monitoring a robot swarm using a data-driven fault detection approach. Robot Autonom Syst 97:193\u2013203. https:\/\/doi.org\/10.1016\/j.robot.2017.06.002","journal-title":"Robot Autonom Syst"},{"issue":"3","key":"1053_CR3","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s10514-018-9733-6","volume":"43","author":"D Park","year":"2019","unstructured":"Park D, Kim H, Kemp CC (2019) Multimodal anomaly detection for assistive robots. Autonom Robots 43(3):611\u2013629. https:\/\/doi.org\/10.1007\/s10514-018-9733-6","journal-title":"Autonom Robots"},{"issue":"1","key":"1053_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3146389","volume":"51","author":"E Khalastchi","year":"2018","unstructured":"Khalastchi E, Kalech M (2018) On fault detection and diagnosis in robotic systems. ACM Comput Surv 51(1):1\u201324. https:\/\/doi.org\/10.1145\/3146389","journal-title":"ACM Comput Surv"},{"key":"1053_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6271017","author":"A Herrero","year":"2019","unstructured":"Herrero A, Jimenez A, Bayraktar S (2019) Hybrid unsupervised exploratory plots: a case study of analysing foreign direct investment. Complexity. https:\/\/doi.org\/10.1155\/2019\/6271017","journal-title":"Complexity"},{"key":"1053_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2686378","author":"X Xu","year":"2019","unstructured":"Xu X, Liu H, Yao M (2019) Recent progress of anomaly detection. Complexity. https:\/\/doi.org\/10.1155\/2019\/2686378","journal-title":"Complexity"},{"key":"1053_CR7","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.neucom.2019.07.034","volume":"363","author":"M Canizo","year":"2019","unstructured":"Canizo M, Triguero I, Conde A, Onieva E (2019) Multi-head cnn-rnn for multi-time series anomaly detection: an industrial case study. Neurocomputing 363:246\u2013260. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.034","journal-title":"Neurocomputing"},{"key":"1053_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04407-2","author":"WA Murtada","year":"2019","unstructured":"Murtada WA, Omran EA (2019) Robust anomaly identification algorithm for noisy signals: spacecraft solar panels model. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04407-2","journal-title":"Neural Comput Appl"},{"issue":"6","key":"1053_CR9","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1007\/s10514-017-9688-z","volume":"42","author":"E Khalastchi","year":"2018","unstructured":"Khalastchi E, Kalech M (2018) A sensor-based approach for fault detection and diagnosis for robotic systems. Autonom Robots 42(6):1231\u20131248. https:\/\/doi.org\/10.1007\/s10514-017-9688-z","journal-title":"Autonom Robots"},{"key":"1053_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.engappai.2019.03.022","volume":"82","author":"J Para","year":"2019","unstructured":"Para J, Del Ser J, Nebro AJ, Zurutuza U, Herrera F (2019) Analyze, sense, preprocess, predict, implement, and deploy (asppid): an incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0. Eng Appl Artif Intell 82:30\u201343. https:\/\/doi.org\/10.1016\/j.engappai.2019.03.022","journal-title":"Eng Appl Artif Intell"},{"key":"1053_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.4995\/riai.2020.13100","volume":"17","author":"JJ Roldan-Gomez","year":"2020","unstructured":"Roldan-Gomez JJ, de Leon J, Garcia-Aunon P, Barrientos A (2020) A review on multi-robot systems: current challenges for operators and new developments of interfaces. Revista Iberoamericana de Autom\u00e1tica e Inform\u00e1tica Industrial 17:294\u2013305. https:\/\/doi.org\/10.4995\/riai.2020.13100","journal-title":"Revista Iberoamericana de Autom\u00e1tica e Inform\u00e1tica Industrial"},{"doi-asserted-by":"publisher","unstructured":"Mao X, Huang H, Wang S (2020) Software engineering for autonomous robot: challenges, progresses and opportunities. In: 2020 27th Asia-Pacific software engineering conference (APSEC), pp 100\u2013108. https:\/\/doi.org\/10.1109\/APSEC51365.2020.00018","key":"1053_CR12","DOI":"10.1109\/APSEC51365.2020.00018"},{"doi-asserted-by":"publisher","unstructured":"Wienke J, Wrede S (2016). A fault detection data set for performance bugs in component-based robotic systems. https:\/\/doi.org\/10.4119\/unibi\/2900911","key":"1053_CR13","DOI":"10.4119\/unibi\/2900911"},{"doi-asserted-by":"publisher","unstructured":"Wienke J, Meyer\u00a0zu Borgsen S, Wrede S (2016) A data set for fault detection research on component-based robotic systems. In: Alboul L, Damian D, Aitken JM (eds) Towards autonomous robotic systems, vol 9716. Springer, Cham, pp 339\u2013350. https:\/\/doi.org\/10.1007\/978-3-319-40379-3_35","key":"1053_CR14","DOI":"10.1007\/978-3-319-40379-3_35"},{"doi-asserted-by":"publisher","unstructured":"Wienke J, Wrede S (2016) Autonomous fault detection for performance bugs in component-based robotic systems. In: 2016 IEEE\/RSJ international conference on intelligent robots and systems (IROS). https:\/\/doi.org\/10.1109\/IROS.2016.7759507. IEEE, pp 3291\u20133297","key":"1053_CR15","DOI":"10.1109\/IROS.2016.7759507"},{"doi-asserted-by":"publisher","unstructured":"Wienke J (2018) Framework-level resouce awareness in robotics and intelligent systems. Phd dissertation, Bielefeld University. https:\/\/doi.org\/10.4119\/unibi\/2932136","key":"1053_CR16","DOI":"10.4119\/unibi\/2932136"},{"key":"1053_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.101","author":"N Basurto","year":"2020","unstructured":"Basurto N, Cambra C (2020) \u00c1lvaro Herrero: improving the detection of robot anomalies by handling data irregularities. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2020.05.101","journal-title":"Neurocomputing"},{"doi-asserted-by":"publisher","unstructured":"Basurto N, Cambra C, Herrero A (2020) Ai-driven visualizations for performance monitoring and anomaly detection in robots. In: 2020 IEEE\/ACS 17th international conference on computer systems and applications (AICCSA). IEEE Computer Society, Los Alamitos, CA, USA, pp 1\u20136. https:\/\/doi.org\/10.1109\/AICCSA50499.2020.9316513","key":"1053_CR18","DOI":"10.1109\/AICCSA50499.2020.9316513"},{"doi-asserted-by":"publisher","unstructured":"Wen X, Chen H (2020) Heterogeneous connection and process anomaly detection of industrial robot in intelligent factory. https:\/\/doi.org\/10.1142\/S0218001420590417","key":"1053_CR19","DOI":"10.1142\/S0218001420590417"},{"key":"1053_CR20","doi-asserted-by":"publisher","first-page":"47072","DOI":"10.1109\/ACCESS.2020.2977892","volume":"8","author":"T Chen","year":"2020","unstructured":"Chen T, Liu X, Xia B, Wang W, Lai Y (2020) Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder. IEEE Access 8:47072\u201347081. https:\/\/doi.org\/10.1109\/ACCESS.2020.2977892","journal-title":"IEEE Access"},{"key":"1053_CR21","doi-asserted-by":"publisher","first-page":"113755","DOI":"10.1016\/J.ESWA.2020.113755","volume":"163","author":"M Castellano-Quero","year":"2021","unstructured":"Castellano-Quero M, Fern\u00e1ndez-Madrigal JA, Garc\u00eda-Cerezo A (2021) Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots. Expert Syst Appl 163:113755. https:\/\/doi.org\/10.1016\/J.ESWA.2020.113755","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1053_CR22","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/72.554199","volume":"8","author":"P Demartines","year":"1997","unstructured":"Demartines P, Herault J (1997) Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans Neural Netw 8(1):148\u2013154. https:\/\/doi.org\/10.1109\/72.554199","journal-title":"IEEE Trans Neural Netw"},{"issue":"5","key":"1053_CR23","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/T-C.1969.222678","volume":"18","author":"JW Sammon","year":"1969","unstructured":"Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput C 18(5):401\u2013409. https:\/\/doi.org\/10.1109\/T-C.1969.222678","journal-title":"IEEE Trans Comput C"},{"key":"1053_CR24","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"1053_CR25","first-page":"833","volume":"15","author":"L van der Maaten","year":"2002","unstructured":"van der Maaten L, Hinton G (2002) Stochastic neighbor embedding. Adv Neural Inf Process Syst 15:833\u2013840","journal-title":"Adv Neural Inf Process Syst"},{"doi-asserted-by":"publisher","unstructured":"Wienke J, Wrede S (2011) A middleware for collaborative research in experimental robotics. In: 2011 IEEE\/SICE international symposium on system integration (SII), pp 1183\u20131190. https:\/\/doi.org\/10.1109\/SII.2011.6147617","key":"1053_CR26","DOI":"10.1109\/SII.2011.6147617"},{"doi-asserted-by":"crossref","unstructured":"Basurto N, Herrero \u00c1 (2020) Data selection to improve anomaly detection in a component-based robot. In: Mart\u00ednez \u00c1lvarez F, Troncoso Lora A, S\u00e1ez Mu\u00f1oz JA, Quinti\u00e1n H, Corchado E (eds) 14th International conference on soft computing models in industrial and environmental applications (SOCO 2019). Springer, Cham, pp 241\u2013250","key":"1053_CR27","DOI":"10.1007\/978-3-030-20055-8_23"},{"issue":"3","key":"1053_CR28","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1145\/3068335","volume":"42","author":"E Schubert","year":"2017","unstructured":"Schubert E, Sander J, Ester M, Kriegel HP, Xu X (2017) Dbscan revisited: why and how you should (still) use dbscan. ACM Trans Database Syst 42(3):19\u201311921. https:\/\/doi.org\/10.1145\/3068335","journal-title":"ACM Trans Database Syst"},{"key":"1053_CR29","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jal.2016.11.026","volume":"24","author":"\u00c1 Arroyo","year":"2017","unstructured":"Arroyo \u00c1, Herrero \u00c1, Tricio V, Corchado E (2017) Analysis of meteorological conditions in Spain by means of clustering techniques. J Appl Logic 24:76\u201389. https:\/\/doi.org\/10.1016\/j.jal.2016.11.026","journal-title":"J Appl Logic"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-021-01053-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-021-01053-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-021-01053-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T04:19:56Z","timestamp":1651205996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-021-01053-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,24]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["1053"],"URL":"https:\/\/doi.org\/10.1007\/s10044-021-01053-0","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2022,1,24]]},"assertion":[{"value":"13 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}