{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:39:19Z","timestamp":1783438759339,"version":"3.54.6"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/N01300X\/2"],"award-info":[{"award-number":["EP\/N01300X\/2"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system\u2019s output, we then introduce our health index measure and use it to train the health index forecasting network.<\/jats:p>","DOI":"10.3390\/s21072547","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T21:30:29Z","timestamp":1617658229000},"page":"2547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3000-418X","authenticated-orcid":false,"given":"Saeid","family":"Safavi","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7258-0812","authenticated-orcid":false,"given":"Mohammad Amin","family":"Safavi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Isfahan University of Technology, Iran 84156-83111, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5030-8703","authenticated-orcid":false,"given":"Hossein","family":"Hamid","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saber","family":"Fallah","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81664","DOI":"10.1109\/ACCESS.2019.2921912","article-title":"Anomaly detection, analysis and prediction techniques in iot environment: A systematic literature review","volume":"7","author":"Fahim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/S1474-6670(17)42524-9","article-title":"Remarks on terminology in the field of supervision, fault detection and diagnosis","volume":"30","year":"1997","journal-title":"IFAC Proc. Vol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques\u2014Part I: Fault diagnosis with model-based and signal-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3166\/ejc.14.359-386","article-title":"Reconfigurable fault-tolerant control: A tutorial introduction","volume":"14","author":"Lunze","year":"2008","journal-title":"Eur. J. Control."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5772\/6230","article-title":"An active fault-tolerant control method ofunmanned underwater vehicles with continuous and uncertain faults","volume":"5","author":"Zhu","year":"2008","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.oceaneng.2017.06.020","article-title":"On intelligent risk analysis and critical decision of underwater robotic vehicle","volume":"140","author":"Xiang","year":"2017","journal-title":"Ocean. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3230708","article-title":"Adaptive online one-class support vector machines with applications in structural health monitoring","volume":"9","author":"Anaissi","year":"2018","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.asoc.2015.10.061","article-title":"Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances","volume":"38","author":"Andrade","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"58336","DOI":"10.1109\/ACCESS.2019.2914236","article-title":"Data-driven remaining useful life prediction considering sensor anomaly detection and data recovery","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","volume":"107","author":"Khan","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., and Schwabacher, M. (2008, January 6\u20139). Metrics for evaluating performance of prognostic techniques. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711436"},{"key":"ref_13","unstructured":"Ran, Y., Zhou, X., Lin, P., Wen, Y., and Deng, R. (2019). A survey of predictive maintenance: Systems, purposes and approaches. arXiv."},{"key":"ref_14","unstructured":"Saxena, A., and Goebel, K. (2019). Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository, NASA Ames."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery health prognostics: A systematic review from data acquisition to RUL prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gaddam, A., Wilkin, T., and Angelova, M. (2019, January 2\u20134). Anomaly Detection Models for Detecting Sensor Faults and Outliers in the IoT-A Survey. Proceedings of the 2019 13th International Conference on Sensing Technology (ICST), Sydney, Australia.","DOI":"10.1109\/ICST46873.2019.9047684"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1754414.1754419","article-title":"Sensor faults: Detection methods and prevalence in real-world datasets","volume":"6","author":"Sharma","year":"2010","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_18","unstructured":"Castillo O\u2019Sullivan, A., and Thierer, A.D. (2020, November 11). Projecting the Growth and Economic Impact of the Internet of Things. Available online: https:\/\/ssrn.com\/abstract=2618794."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10115-011-0474-5","article-title":"In-network outlier detection in wireless sensor networks","volume":"34","author":"Branch","year":"2013","journal-title":"Knowl. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.procs.2015.10.026","article-title":"Anomaly detection in medical wireless sensor networks using machine learning algorithms","volume":"70","author":"Pachauri","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","unstructured":"Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V.H., M\u00fchlegg, M., and Dorn, S. (2020, August 10). A2D2: Audi Autonomous Driving Dataset 2020, Available online: http:\/\/xxx.lanl.gov\/abs\/2004.06320."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1007\/s13198-017-0573-0","article-title":"Risk assessment of sensor failures in a condition monitoring process; degradation-based failure probability determination","volume":"8","year":"2017","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/JSEN.2009.2030284","article-title":"Modeling, detection, and disambiguation of sensor faults for aerospace applications","volume":"9","author":"Balaban","year":"2009","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1109\/ACCESS.2017.2705644","article-title":"Sensor fault classification based on support vector machine and statistical time-domain features","volume":"5","author":"Jan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.isatra.2016.11.005","article-title":"Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV","volume":"67","author":"Abbaspour","year":"2017","journal-title":"ISA Trans."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Abid, K., Mouchaweh, M.S., and Cornez, L. (2018). Fault prognostics for the predictive maintenance of wind turbines: State of the art. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-030-14880-5_10"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1016\/j.ymssp.2009.01.009","article-title":"Gear crack level identification based on weighted K nearest neighbor classification algorithm","volume":"23","author":"Lei","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Biddle, L., and Fallah, S. (2021). A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM. J. Automot. Innov., accepted.","DOI":"10.1007\/s42154-021-00138-0"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, G., Zhang, G., Ge, Q., and Liu, X. (2016, January 19\u201321). Research advances in fault diagnosis and prognostic based on deep learning. Proceedings of the 2016 Prognostics and system health management conference (PHM-Chengdu), Chengdu, China.","DOI":"10.1109\/PHM.2016.7819786"},{"key":"ref_31","unstructured":"Lim, B., Arik, S.O., Loeff, N., and Pfister, T. (2019). Temporal fusion transformers for interpretable multi-horizon time series forecasting. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., and Yang, R. (2018, January 18\u201322). The apolloscape dataset for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_35","unstructured":"Wen, R., Torkkola, K., Narayanaswamy, B., and Madeka, D. (2017). A multi-horizon quantile recurrent forecaster. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2547\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:38Z","timestamp":1760363918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072547"],"URL":"https:\/\/doi.org\/10.3390\/s21072547","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,5]]}}}