{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T09:47:08Z","timestamp":1781862428486,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T00:00:00Z","timestamp":1710028800000},"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>Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.<\/jats:p>","DOI":"10.3390\/s24061783","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T04:51:12Z","timestamp":1710132672000},"page":"1783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9491-6663","authenticated-orcid":false,"given":"Monica","family":"Tiboni","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6982-2999","authenticated-orcid":false,"given":"Carlo","family":"Remino","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ao, S.I., Gelman, L., Karimi, H.R., and Tiboni, M. (2022). Advances in Machine Learning for Sensing and Condition Monitoring. Appl. Sci., 12.","DOI":"10.3390\/app122312392"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"022003","DOI":"10.1088\/1361-6501\/ad0f67","article-title":"Latest innovations in the field of condition-based maintenance of rotatory machinery: A review","volume":"35","author":"Kumar","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1109\/TMECH.2021.3132459","article-title":"Fault Detection in Gears Using Fault Samples Enlarged by a Combination of Numerical Simulation and a Generative Adversarial Network","volume":"27","author":"Gao","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiang, J., and Zhong, Y. (2016). A Novel Personalized Diagnosis Methodology Using Numerical Simulation and an Intelligent Method to Detect Faults in a Shaft. Appl. Sci., 6.","DOI":"10.3390\/app6120414"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tiboni, M., Bussola, R., Aggogeri, F., and Amici, C. (2020). Experimental and Model-Based Study of the Vibrations in the Load Cell Response of Automatic Weight Fillers. Electronics, 9.","DOI":"10.3390\/electronics9060995"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108833","DOI":"10.1016\/j.triboint.2023.108833","article-title":"Artificial neural network for tilting pad journal bearing characterization","volume":"188","author":"Gheller","year":"2023","journal-title":"Tribol. Int."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Briglia, G., Immovilli, F., Cocconcelli, M., and Lippi, M. (2023). Bearing Fault Detection and Recognition from Supply Currents with Decision Trees, IEEE Access.","DOI":"10.1109\/ACCESS.2023.3348245"},{"key":"ref_9","unstructured":"Cocconcelli, M., Rubini, R., Zimroz, R., and Bartelmus, W. (2011, January 20\u201322). Diagnostics of ball bearings in varying-speed motors by means of Artificial Neural Networks. Proceedings of the 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2011\/MFPT 2011, Cardiff, UK."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109768","DOI":"10.1016\/j.ress.2023.109768","article-title":"A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size","volume":"242","author":"Kumar","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/TIA.2010.2049623","article-title":"Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison","volume":"46","author":"Immovilli","year":"2010","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"388","DOI":"10.55549\/epstem.1371758","article-title":"Internet of Things (IoT): Wireless Communications for Unmanned Aircraft System","volume":"23","author":"Vo","year":"2023","journal-title":"Eurasia Proc. Sci. Technol. Eng. Math."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Viale, L., Daga, A.P., Fasana, A., and Garibaldi, L. (2022). From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine. Machines, 10.","DOI":"10.3390\/machines10040270"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1177\/1077546320909975","article-title":"Comparison of single-input single-output and multi-input multi-output control strategies for performing sequential single-axis random vibration control test","volume":"26","author":"Mucchi","year":"2020","journal-title":"J. Vib. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012024","DOI":"10.1088\/1742-6596\/2507\/1\/012024","article-title":"Predictive maintenance of wind turbine\u2019s main bearing using wind farm SCADA data and LSTM neural networks","volume":"2507","author":"Vidal","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"034501","DOI":"10.1115\/1.4048490","article-title":"Wind Turbine Multivariate Power Modeling Techniques for Control and Monitoring Purposes","volume":"143","author":"Astolfi","year":"2020","journal-title":"J. Dyn. Syst. Meas. Control"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Natili, F., Daga, A.P., Castellani, F., and Garibaldi, L. (2021). Multi-Scale Wind Turbine Bearings Supervision Techniques Using Industrial SCADA and Vibration Data. Appl. Sci., 11.","DOI":"10.3390\/app11156785"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","article-title":"A review of process fault detection and diagnosis: Part I: Quantitative model-based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1007\/s13198-021-01467-3","article-title":"Multi-agent task planning and resource apportionment in a smart grid","volume":"13","author":"Chen","year":"2022","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","article-title":"A review of fault detection and diagnosis. Part III: Process history based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0098-1354(02)00161-8","article-title":"A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/14484846.2009.11464588","article-title":"Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review","volume":"7","author":"Jayaswalt","year":"2009","journal-title":"Aust. J. Mech. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Suzuki, K. (2011). Artificial Neural Networks\u2014Industrial and Control Engineering Applications, IntechOpen.","DOI":"10.5772\/2041"},{"key":"ref_24","first-page":"28","article-title":"Neural networks for pneumatic actuator fault detection","volume":"90","author":"MacLeod","year":"1999","journal-title":"Trans. S. Afr. Inst. Electr. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0952-1976(02)00068-4","article-title":"Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator","volume":"15","author":"Karpenko","year":"2002","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1016\/S0967-0661(02)00245-9","article-title":"Diagnosis of process valve actuator faults using a multilayer neural network","volume":"11","author":"Karpenko","year":"2003","journal-title":"Control Eng. Pract."},{"key":"ref_27","first-page":"43","article-title":"Artificial Neural Network Approach for Fault Detection in Pneumatic Valve in Cooler Water Spray System","volume":"9","author":"Subbaraj","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nagamalai, D., Renault, E., and Dhanuskodi, M. (2011, January 23\u201325). Fault Diagnosis of Pneumatic Valve Using PCA and ANN Techniques. Proceedings of the Trends in Computer Science, Engineering and Information Technology, Tirunelveli, India.","DOI":"10.1007\/978-3-642-24043-0"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.asoc.2014.02.008","article-title":"Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach","volume":"19","author":"Subbaraj","year":"2014","journal-title":"Appl. Soft Comput. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.conengprac.2005.09.006","article-title":"Introduction to the DAMADICS actuator FDI benchmark study","volume":"14","author":"Patton","year":"2006","journal-title":"Control Eng. Pract."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"453169","DOI":"10.1155\/2011\/453169","article-title":"Early FDI Based on Residuals Design According to the Analysis of Models of Faults: Application to DAMADICS","volume":"2011","author":"Kourd","year":"2011","journal-title":"Adv. Artif. Neural Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, F., Shang, Q., and Yu, S. (2011, January 28\u201330). Fault diagnosis of the pneumatic actuators based on neural network. Proceedings of the 4th International Symposium on Computational Intelligence and Design (ISCID 2011), Hangzhou, China.","DOI":"10.1109\/ISCID.2011.68"},{"key":"ref_33","first-page":"11","article-title":"Fault Diagnosis of Pneumatic Valve with DAMADICS Simulator using ANN based Classifier Approach","volume":"1","author":"Sundarmahesh","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_34","first-page":"138","article-title":"Fault Diagnosis in Process Control Valve Using Artificial Neural Network","volume":"3","author":"Prabakaran","year":"2013","journal-title":"Int. J. Innov. Appl. Stud."},{"key":"ref_35","first-page":"1361","article-title":"Self-Organizing Map Based Fault Detection and Isolation Scheme for Pneumatic Actuator","volume":"3","author":"Prabakaran","year":"2014","journal-title":"Int. J. Innov. Appl. Stud."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"702","DOI":"10.15623\/ijret.2014.0319125","article-title":"Principal Component Analysis Based Approach for Fault Diagnosis in Pneumatic Valve Using Damadics Benchmark Simulator","volume":"3","author":"Kowsalya","year":"2014","journal-title":"Int. J. Res. Eng. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Andrade, A., Lopes, K., Lima, B., and Maitelli, A. (2021). Development of a methodology using artificial neural network in the detection and diagnosis of faults for pneumatic control valves. Sensors, 21.","DOI":"10.3390\/s21030853"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10512","DOI":"10.1016\/j.eswa.2009.01.028","article-title":"Fault diagnosis of pneumatic systems with artificial neural network algorithms","volume":"36","author":"Demetgul","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"310","DOI":"10.20965\/ijat.2019.p0310","article-title":"Smart Modular Architecture for Supervision and Monitoring of a 4.0 Production Plant","volume":"13","author":"Tiboni","year":"2019","journal-title":"Int. J. Autom. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/978-3-030-11220-2_19","article-title":"Comparison of signal processing techniques for condition monitoring based on artificial neural networks","volume":"15","author":"Tiboni","year":"2019","journal-title":"Appl. Cond. Monit."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Caesarendra, W., and Tjahjowidodo, T. (2017). A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines, 5.","DOI":"10.3390\/machines5040021"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tiboni, M., Remino, C., Bussola, R., and Amici, C. (2022). A Review on Vibration-Based Condition Monitoring of Rotating Machinery. Appl. Sci., 12.","DOI":"10.3390\/app12030972"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"108385","DOI":"10.1016\/j.ymssp.2021.108385","article-title":"A review of online condition monitoring and maintenance strategy for cylinder liner-piston rings of diesel engines","volume":"165","author":"Rao","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_45","unstructured":"Tiboni, M., and Remino, C. (2017, January 13\u201316). Condition monitoring of a mechanical indexing system with artificial neural networks. Proceedings of the WCCM 2017\u20141st World Congress on Condition Monitoring 2017, London, UK."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"110622","DOI":"10.1016\/j.measurement.2021.110622","article-title":"A new tool wear condition monitoring method based on deep learning under small samples","volume":"189","author":"Zhou","year":"2022","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:11:32Z","timestamp":1760105492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,10]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24061783"],"URL":"https:\/\/doi.org\/10.3390\/s24061783","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,10]]}}}