{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T08:06:30Z","timestamp":1790669190739,"version":"4.1.0"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"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>Industrial robots are widely used in critical tasks such as assembly, welding, and material handling as core components of modern manufacturing systems. For the reliable operation of these systems, early and accurate detection of execution failures is crucial. In this study, a comprehensive comparison of machine learning and deep learning methods is conducted for the classification of robot execution failures using data acquired from force\u2013torque sensors. Three different feature engineering approaches are proposed. The first is a Baseline approach that includes 90 raw time-series features. The second is the Domain-6 approach, which consists of 6 basic statistical features per sensor (36 in total). The third is the Domain-12 approach, which comprises 12 comprehensive statistical features per sensor (72 in total). The domain features include the mean, standard deviation, minimum, maximum, range, slope, median, skewness, kurtosis, RMS, energy, and IQR. In total, ten classification algorithms are evaluated, including eight machine learning methods and two deep learning models: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Trees (DT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM-LGBM), as well as a One-Dimensional Convolutional Neural Network (CNN-1D) and Long Short-Term Memory (LSTM). For traditional machine learning algorithms, 5 \u00d7 5 nested cross-validation is used, whereas for deep learning models, 5-fold cross-validation with a 20% validation split is employed. To ensure statistical reliability, all experiments are repeated over 30 independent runs. The experimental results demonstrate that feature engineering has a decisive impact on classification performance. In addition, regardless of the feature set, the highest accuracy (93.85% \u00b1 0.90) is achieved by the Naive Bayes classifier using the Baseline features. The Domain-12 feature set provides consistent improvements across many algorithms, with substantial performance gains. The results are reported using accuracy, precision, recall, and F1-score metrics and are supported by confusion matrices. Finally, permutation feature importance analysis indicates that the skewness features of the Fx and Fy sensors are the most critical variables for failure detection. Overall, these findings show that time-domain statistical features offer an effective approach for robot failure classification.<\/jats:p>","DOI":"10.3390\/s26051649","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:19:21Z","timestamp":1772720361000},"page":"1649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Statistical Feature Engineering for Robot Failure Detection: A Comparative Study of Machine Learning and Deep Learning Classifiers"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8096-1140","authenticated-orcid":false,"given":"Serta\u00e7","family":"Sava\u015f","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, Erciyes University, 38039 Kayseri, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, H., and Motoda, H. (1998). Feature Transformation Strategies for a Robot Learning Problem. Feature Extraction, Construction and Selection, Springer.","DOI":"10.1007\/978-1-4615-5725-8"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Eang, C., and Lee, S. (2025). Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers. Sensors, 25.","DOI":"10.3390\/s25010025"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"31422","DOI":"10.1109\/JIOT.2024.3418352","article-title":"Online Fault Diagnosis of Industrial Robot Using IoRT and Hybrid Deep Learning Techniques: An Experimental Approach","volume":"11","author":"Bilal","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s43684-022-00030-6","article-title":"An attention enhanced dilated CNN approach for cross-axis industrial robotics fault diagnosis","volume":"2","author":"Liu","year":"2022","journal-title":"Auton. Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pan, J., Qu, L., and Peng, K. (2021). Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN. Entropy, 23.","DOI":"10.3390\/e23060751"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7929","DOI":"10.1109\/TIE.2019.2931511","article-title":"Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling","volume":"67","author":"Sabry","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isatra.2020.03.021","article-title":"Interacting multiple model-based adaptive control system for stable steering of distributed driver electric vehicle under various road excitations","volume":"103","author":"Shi","year":"2020","journal-title":"ISA Trans."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10845-018-1456-1","article-title":"Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation","volume":"31","author":"Li","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.3934\/mbe.2024105","article-title":"Bearing fault diagnosis with parallel CNN and LSTM","volume":"21","author":"Fu","year":"2024","journal-title":"Math. Biosci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10845-020-01600-2","article-title":"Bearing fault diagnosis base on multi-scale CNN and LSTM model","volume":"32","author":"Chen","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, W., and Guo, J. (2024). Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model. Machines, 12.","DOI":"10.3390\/machines12120927"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rohan, A., Raouf, I., and Kim, H.S. (2020). Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM). Sensors, 20.","DOI":"10.3390\/s20236845"},{"key":"ref_13","first-page":"417","article-title":"Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: A data-driven approach","volume":"9","author":"Raouf","year":"2022","journal-title":"J. Comput. Des. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fu, X., Yang, K., Liu, M., Xing, T., and Wu, C. (2022). LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers. Sensors, 22.","DOI":"10.3390\/s22145296"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2735335","DOI":"10.1155\/2024\/2735335","article-title":"Research on Local Fault Diagnosis of Rotary Vector Reducer Based on Motor Current Signature Analysis","volume":"2024","author":"Li","year":"2024","journal-title":"Int. J. Rotating Mach."},{"key":"ref_16","unstructured":"Lopes, L., and Camarinha-Matos, L. (2026, February 02). Robot Execution Failures [Dataset]. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/138\/robot+execution+failures."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_21","unstructured":"John, G.H., and Langley, P. (1995, January 18\u201320). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montr\u00e9al, QC, Canada."},{"key":"ref_22","unstructured":"Breiman, L., Friedman, J., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, CRC Press. [1st ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), San Francisco, CA, USA, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1649\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:20:43Z","timestamp":1772720443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26051649"],"URL":"https:\/\/doi.org\/10.3390\/s26051649","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,5]]}}}