The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VSB Technical University of Ostrava 17 Listopadu 2172 15 70800 Ostrava Czech Republic

Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Avadi 600062 India

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Chen P., Liu H., Xin R., Carval T., Zhao J., Xia Y., Zhao Z. Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model. Comput. J. 2022;65:2909–2925. doi: 10.1093/comjnl/bxac085. DOI

Pamula A.S.P., Ravilla A., Madiraju S.V.H. Applications of the Internet of Things (IoT) in Real-Time Monitoring of Contaminants in the Air, Water, and Soil. Eng. Proc. 2022;27:26. doi: 10.3390/ecsa-9-13335. DOI

Cheng B., Zhu D., Zhao S., Chen J. Situation-Aware IoT Service Coordination Using the Event-Driven SOA Paradigm. IEEE Trans. Netw. Serv. Manag. 2016;13:349–361. doi: 10.1109/TNSM.2016.2541171. DOI

Min H., Fang Y., Wu X., Lei X., Chen S., Teixeira R., Zhu B., Zhao X., Xu Z. A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis. Expert Syst. Appl. 2023;224:120002. doi: 10.1016/j.eswa.2023.120002. DOI

Ma X., Dong Z., Quan W., Dong Y., Tan Y. Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm. Mech. Syst. Signal Process. 2023;187:109930. doi: 10.1016/j.ymssp.2022.109930. DOI

Burhan M., Rehman R.A., Khan B., Kim B.S. IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey. Sensors. 2018;18:2796. doi: 10.3390/s18092796. PubMed DOI PMC

Lombardi M., Pascale F., Santaniello D. Internet of Things: A General Overview between Architectures, Protocols and Applications. Information. 2021;12:87. doi: 10.3390/info12020087. DOI

Ma K., Li Z., Liu P., Yang J., Geng Y., Yang B., Guan X. Reliability-Constrained Throughput Optimization of Industrial Wireless Sensor Networks With Energy Harvesting Relay. IEEE Internet Things J. 2021;8:13343–13354. doi: 10.1109/JIOT.2021.3065966. DOI

Xu S., Huang W., Huang D., Chen H., Chai Y., Ma M., Zheng W.X. A Reduced-Order Observer-Based Method for Simultaneous Diagnosis of Open-Switch and Current Sensor Faults of a Grid-Tied NPC Inverter. IEEE Trans. Power Electron. 2023;38:9019–9032. doi: 10.1109/TPEL.2023.3268666. DOI

Ayoub I., Balakrichenan S., Khawam K., Ampeau B. DNS for IoT: A Survey. Sensors. 2023;23:4473. doi: 10.3390/s23094473. PubMed DOI PMC

Chen Y., Zhen Z., Yu H., Xu J. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture. Sensors. 2017;17:153. doi: 10.3390/s17010153. PubMed DOI PMC

Salunke R., Nobahar M., Alzeghoul O.E., Khan S., La Cour I., Amini F. Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images. Remote Sens. 2023;15:1888. doi: 10.3390/rs15071888. DOI

Gu Y., Zheng G. Dynamic Evolution Characteristics of the Gear Meshing Lubrication for Vehicle Transmission System. Processes. 2023;11:561. doi: 10.3390/pr11020561. DOI

Li L., Tan Y., Xu W., Ni Y., Yang J., Tan D. Fluid-induced transport dynamics and vibration patterns of multiphase vortex in the critical transition states. Int. J. Mech. Sci. 2023;252:108376. doi: 10.1016/j.ijmecsci.2023.108376. DOI

Lu M.-C., Huang Q.-X., Chiu M.-Y., Tsai Y.-C., Sun H.-M. PSPS: A Step toward Tamper Resistance against Physical Computer Intrusion. Sensors. 2022;22:1882. doi: 10.3390/s22051882. PubMed DOI PMC

Gaddam A., Wilkin T., Angelova M., Gaddam J. Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions. Electronics. 2020;9:511. doi: 10.3390/electronics9030511. DOI

Markulik S., Šolc M., Petrík J., Balážiková M., Blaško P., Kliment J., Bezák M. Application of FTA Analysis for Calculation of the Probability of the Failure of the Pressure Leaching Process. Appl. Sci. 2021;11:6731. doi: 10.3390/app11156731. DOI

Antonini M., Pincheira M., Vecchio M., Antonelli F. An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments. Sensors. 2013;23:2344. doi: 10.3390/s23042344. PubMed DOI PMC

Rojek I., Jasiulewicz-Kaczmarek M., Piechowski M., Mikołajewski D. An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair. Appl. Sci. 2023;13:4971. doi: 10.3390/app13084971. DOI

Liu Y., Ma X., Qiao W., Luo H., He P. Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network. Int. J. Environ. Res. Public Health. 2021;19:297. doi: 10.3390/ijerph19010297. PubMed DOI PMC

Adday G.H., Subramaniam S.K., Zukarnain Z.A., Samian N. Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions. Sensors. 2022;22:6041. doi: 10.3390/s22166041. PubMed DOI PMC

Soltanali H., Khojastehpour M., Farinha J.T., Pais J.E.d.A.e. An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing. Energies. 2021;14:7758. doi: 10.3390/en14227758. DOI

Santo Y., Immich R., Dalmazo B.L., Riker A. Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things. Sensors. 2023;23:3544. doi: 10.3390/s23073544. PubMed DOI PMC

Ding X., Wang H., Cao Z., Liu X., Liu Y., Huang Z. An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network. Electronics. 2023;12:1816. doi: 10.3390/electronics12081816. DOI

Gültekin Ö., Cinar E., Özkan K., Yazıcı A. Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence. Sensors. 2022;22:3208. doi: 10.3390/s22093208. PubMed DOI PMC

Bruneo D., De Vita F. Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks. Sensors. 2022;22:2858. doi: 10.3390/s22082858. PubMed DOI PMC

Morenas J.d.L., Moya-Fernández F., López-Gómez J.A. The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines. Sensors. 2023;23:2649. doi: 10.3390/s23052649. PubMed DOI PMC

Cao B., Zhao J., Gu Y., Fan S., Yang P. Security-Aware Industrial Wireless Sensor Network Deployment Optimization. IEEE Trans. Ind. Inform. 2019;16:5309–5316. doi: 10.1109/TII.2019.2961340. DOI

Liu X., He J., Liu M., Yin Z., Yin L., Zheng W. A Scenario-Generic Neural Machine Translation Data Augmentation Method. Electronics. 2023;12:2320. doi: 10.3390/electronics12102320. DOI

Raposo D., Rodrigues A., Silva J.S., Boavida F. A Taxonomy of Faults for Wireless Sensor Networks. J. Netw. Syst. Manag. 2017;25:591–611. doi: 10.1007/s10922-017-9403-6. DOI

Welcer M., Szczepański C., Krawczyk M. The Impact of Sensor Errors on Flight Stability. Aerospace. 2022;9:169. doi: 10.3390/aerospace9030169. DOI

Meng Y., Wu X., Oladejo J., Dong X., Zhang Z., Deng J., Yan Y., Zhao H., Lester E., Wu T., et al. Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis. ChemBioEng Rev. 2021;8:535–544. doi: 10.1002/cben.202100008. DOI

Zhang Z., Mehmood A., Shu L., Huo Z., Zhang Y., Mukherjee M. A Survey on Fault Diagnosis in Wireless Sensor Networks. IEEE Access. 2018;6:11349–11364. doi: 10.1109/ACCESS.2018.2794519. DOI

Lau B.C., Ma E.W., Chow T.W. Probabilistic fault detector for Wireless Sensor Network. Expert Syst. Appl. 2014;41:3703–3711. doi: 10.1016/j.eswa.2013.11.034. DOI

Zidi S., Moulahi T., Alaya B. Fault Detection in Wireless Sensor Networks Through SVM Classifier. IEEE Sens. J. 2017;18:340–347. doi: 10.1109/JSEN.2017.2771226. DOI

Javaid A., Javaid N., Wadud Z., Saba T., Sheta O.E., Saleem M.Q., Alzahrani M.E. Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks. Sensors. 2019;19:1334. doi: 10.3390/s19061334. PubMed DOI PMC

Haque S.A., Rahman M., Aziz S.M. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare. Sensors. 2015;15:8764–8786. doi: 10.3390/s150408764. PubMed DOI PMC

Takahashi M., Anang Y., Watanabe Y. A Proposal of Fault Tree Analysis for Embedded Control Software. Information. 2020;11:402. doi: 10.3390/info11090402. DOI

Byun S., Papaelias M., Márquez F.P.G., Lee D. Fault-Tree-Analysis-Based Health Monitoring for Autonomous Underwater Vehicle. J. Mar. Sci. Eng. 2022;10:1855. doi: 10.3390/jmse10121855. DOI

Chen G. Fault Diagnosis Method Based on System-phenomenon-fault Tree. Chin. J. Mech. Eng. 2011;24:466–473. doi: 10.3901/CJME.2011.03.466. DOI

Mentes A., Helvacioglu I.H. An application of fuzzy fault tree analysis for spread mooring systems. Ocean Eng. 2011;38:285–294. doi: 10.1016/j.oceaneng.2010.11.003. DOI

Wang Y., Li Q., Chang M., Chen H., Zang G. Research on Fault Diagnosis Expert System Based on the Neural Network and the Fault Tree Technology. Procedia Eng. 2012;31:1206–1210. doi: 10.1016/j.proeng.2012.01.1164. DOI

Zhao M., Tian Z., Chow T.W.S. Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation. Neural Comput. Appl. 2018;31:4019–4030. doi: 10.1007/s00521-018-3342-3. DOI

Swain R.R., Khilar P.M., Dash T. Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network. Digit. Commun. Netw. 2020;6:86–100. doi: 10.1016/j.dcan.2018.02.001. DOI

Aldhafeeri T., Tran M.-K., Vrolyk R., Pope M., Fowler M. A Review of Methane Gas Detection Sensors: Recent Developments and Future Perspectives. Inventions. 2020;5:28. doi: 10.3390/inventions5030028. DOI

Shanmugasundar G., Vanitha M., Čep R., Kumar V., Kalita K., Ramachandran M. A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining. Processes. 2021;9:2015. doi: 10.3390/pr9112015. DOI

Liu Y., Wang Y., Zhang J. Information Computing and Applications. Volume 7473. Springer; Berlin/Heidelberg, Germany: 2012. New machine learning algorithm: Random forest; pp. 246–252. Lecture Notes in Computer Science. DOI

Shaik K., Ramesh J.V.N., Mahdal M., Rahman M.Z.U., Khasim S., Kalita K. Big Data Analytics Framework Using Squirrel Search Optimized Gradient Boosted Decision Tree for Heart Disease Diagnosis. Appl. Sci. 2023;13:5236. doi: 10.3390/app13095236. DOI

Ganesh N., Shankar R., Čep R., Chakraborty S., Kalita K. Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm. Appl. Sci. 2023;13:3223. doi: 10.3390/app13053223. DOI

Li R., Wu X., Tian H., Yu N., Wang C. Hybrid Memetic Pretrained Factor Analysis-Based Deep Belief Networks for Transient Electromagnetic Inversion. IEEE Trans. Geosci. Remote Sens. 2022;60:1–20. doi: 10.1109/TGRS.2022.3208465. DOI

Li J., Deng Y., Sun W., Li W., Li R., Li Q., Liu Z. Resource Orchestration of Cloud-Edge–based Smart Grid Fault Detection. ACM Trans. Sens. Netw. 2022;18:1–26. doi: 10.1145/3529509. DOI