{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:57:54Z","timestamp":1775807874010,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T00:00:00Z","timestamp":1640044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Robotics Programme under its Robotics Enabling Capabilities and Technologies","award":["Funding Agency Project No. 192 25 00051,  192 22 00058, 192 22 00108"],"award-info":[{"award-number":["Funding Agency Project No. 192 25 00051,  192 22 00058, 192 22 00108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot \u2018Snail\u2019 with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.<\/jats:p>","DOI":"10.3390\/s22010013","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T09:50:43Z","timestamp":1640080243000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-9286","authenticated-orcid":false,"given":"Sathian","family":"Pookkuttath","sequence":"first","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6504-1530","authenticated-orcid":false,"given":"Mohan","family":"Rajesh Elara","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2519-8327","authenticated-orcid":false,"given":"Vinu","family":"Sivanantham","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-9814","authenticated-orcid":false,"given":"Balakrishnan","family":"Ramalingam","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"key":"ref_1","unstructured":"Research and Markets (2021, June 10). Worldwide Cleaning Robot Industry to 2026-Key Market Drivers and Restraints. Available online: https:\/\/www.prnewswire.com\/news-releases\/worldwide-cleaning-robot-industry-to-2026\u2014key-market-drivers-and-restraints-301293632.html."},{"key":"ref_2","unstructured":"Huang, H.P., and Wu, S.H. (2011, January 21\u201325). Diagnostic and predictive maintenance systems for abnormal behavior of power scheduling loading and its application to robotics systems. Proceedings of the 2011 9th World Congress on Intelligent Control and Automation, Taipei, Taiwan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Izagirre, U., Andonegui, I., Egea, A., and Zurutuza, U. (2020). A methodology and experimental implementation for industrial robot health assessment via torque signature analysis. Appl. Sci., 10.","DOI":"10.3390\/app10217883"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133123","DOI":"10.1109\/ACCESS.2021.3114505","article-title":"Programmable Motion-Fault Detection for a Collaborative Robot","volume":"9","author":"Park","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102177","DOI":"10.1016\/j.rcim.2021.102177","article-title":"Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots","volume":"71","author":"Aivaliotis","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Borgi, T., Hidri, A., Neef, B., and Naceur, M.S. (2017, January 14\u201317). Data analytics for predictive maintenance of industrial robots. Proceedings of the 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET), Hammamet, Tunisia.","DOI":"10.1109\/ASET.2017.7983729"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, H.G., Yoon, H.S., Yoo, J.H., Yoon, H.I., and Han, S.S. (2019, January 22\u201325). Development of Predictive Maintenance Technology for Wafer Transfer Robot using Clustering Algorithm. Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand.","DOI":"10.23919\/ELINFOCOM.2019.8706485"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aliev, K., and Antonelli, D. (2021). Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Appl. Sci., 11.","DOI":"10.3390\/app11041621"},{"key":"ref_9","unstructured":"Onur, K., Kaymakci, O.T., and Mercimek, M. (2020, January 21\u201323). Advanced Predictive Maintenance with Machine Learning Failure Estimation in Industrial Packaging Robots. Proceedings of the 2020 International Conference on Development and Application Systems (DAS), Suceava, Romania."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pierleoni, P., Belli, A., Palma, L., and Sabbatini, L. (2021, January 27\u201328). Diagnosis and Prognosis of a Cartesian Robot\u2019s Drive Belt Looseness. Proceedings of the 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), BALI, Indonesia.","DOI":"10.1109\/IoTaIS50849.2021.9359712"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, D., and Wang, X. (2020, January 27\u201329). Fault diagnosis of industrial robots based on multi-sensor information fusion and 1D convolutional neural network. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9189568"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1177\/1077546319859704","article-title":"Vibration based brake health monitoring using wavelet features: A machine learning approach","volume":"25","author":"Jegadeeshwaran","year":"2019","journal-title":"J. Vib. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s10514-007-9077-0","article-title":"Frequency response method for terrain classification in autonomous ground vehicles","volume":"24","author":"Dupont","year":"2008","journal-title":"Auton. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10846-020-01293-y","article-title":"Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis","volume":"101","author":"Gomes","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Safavi, S., Safavi, M.A., Hamid, H., and Fallah, S. (2021). Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles. Sensors, 21.","DOI":"10.3390\/s21072547"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11265-018-1378-3","article-title":"A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier","volume":"91","author":"Eren","year":"2019","journal-title":"J. Signal Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8136","DOI":"10.1109\/TIE.2018.2886789","article-title":"Fault detection and severity identification of ball bearings by online condition monitoring","volume":"66","author":"Abdeljaber","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-time motor fault detection by 1-D convolutional neural networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.neucom.2017.09.069","article-title":"1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data","volume":"275","author":"Abdeljaber","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jsv.2018.03.008","article-title":"Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks","volume":"424","author":"Avci","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5766","DOI":"10.1049\/iet-gtd.2020.0773","article-title":"1D-CNN based real-time fault detection system for power asset diagnostics","volume":"14","author":"Mitiche","year":"2020","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Toh, G., and Park, J. (2020). Review of vibration-based structural health monitoring using deep learning. Appl. Sci., 10.","DOI":"10.3390\/app10051680"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Kim, J.M., and Kim, C.H. (2020). Accurate bearing fault diagnosis under variable shaft speed using convolutional neural networks and vibration spectrogram. Appl. Sci., 10.","DOI":"10.3390\/app10186385"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kolar, D., Lisjak, D., Paj\u0105k, M., and Pavkovi\u0107, D. (2020). Fault diagnosis of rotary machines using deep convolutional neural network with wide three axis vibration signal input. Sensors, 20.","DOI":"10.3390\/s20144017"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.microrel.2017.03.006","article-title":"Deep neural networks-based rolling bearing fault diagnosis","volume":"75","author":"Chen","year":"2017","journal-title":"Microelectron. Reliab."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, H.Y., and Lee, C.H. (2021). Deep Learning Approach for Vibration Signals Applications. Sensors, 21.","DOI":"10.3390\/s21113929"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIE.2018.2807414","article-title":"Early fault detection of machine tools based on deep learning and dynamic identification","volume":"66","author":"Luo","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"015116","DOI":"10.1063\/1.5118000","article-title":"Fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes","volume":"91","author":"Wu","year":"2020","journal-title":"Rev. Sci. Instrum."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tibebu, H., Roche, J., De Silva, V., and Kondoz, A. (2021). LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping. Sensors, 21.","DOI":"10.3390\/s21072263"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Foster, P., Sun, Z., Park, J.J., and Kuipers, B. (2013, January 6\u201310). Visagge: Visible angle grid for glass environments. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630875"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Schwartz, M., and Zarzycki, A. (2017, January 20\u201322). The effect of building materials on LIDAR measurements. Proceedings of the 35th eCAADe Conference-Volume 2, Rome, Italy.","DOI":"10.52842\/conf.ecaade.2017.2.269"},{"key":"ref_34","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_37","unstructured":"Marius, H. (2020). Multiclass Classificaton with Support Vector Machines (SVM) Dual Problem and Kernel Function. Towards Data Sci., Available online: https:\/\/towardsdatascience.com\/multiclass-classification-with-support-vector-machines-svm-kernel-trick-kernel-functions-f9d5377d6f02."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Taud, H., and Mas, J. (2018). Multilayer perceptron (MLP). Geomatic Approaches for Modeling Land Change Scenarios, Springer.","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_40","unstructured":"Mohamad, T.H., Abbasi, A., Kim, E., and Nataraj, C. (2021, January 7\u20139). Application of Deep CNN-LSTM Network to Gear Fault Diagnostics. Proceedings of the 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit (Romulus), MI, USA."},{"key":"ref_41","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:34Z","timestamp":1760169034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,21]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010013"],"URL":"https:\/\/doi.org\/10.3390\/s22010013","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,21]]}}}