Abstract
In the context of automotive and two-wheeled vehicles, the comfort and safety of drivers and passengers is even more entrusted to electronic systems which are closed-loop systems generally implementing suitable control strategies on the basis of measurements provided by a set of sensors. Therefore, the development of proper instrument fault detection schemes able to identify faults occurring on the sensors involved in the closed-loop are crucial for warranting the effectiveness and the reliability of such strategies. In this framework, the paper describes a virtual sensor based on a Nonlinear Auto-Regressive with eXogenous inputs (NARX) artificial neural network for instrument fault diagnosis of the linear potentiometer sensor employed in motorcycle semi-active suspension systems. The use of such a model has been suggested by the particular ability of NARX in effectively take into account for the system nonlinearities. The proposed soft sensor has been designed, trained and tuned on the basis of real samples acquired on the field in different operating conditions of a real motorcycle. The achieved results, show that the proposed diagnostic scheme is characterized by very interesting features in terms of promptness and sensitivity in detecting also “small faults”.
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D’Angelo, G., Laracca, M., Rampone, S., Betta, G.: Fast eddy current testing defect classification using Lissajous figures. IEEE Trans. Instrum. Meas. 67(4), 821–830 (2018)
Bernieri, A., Ferrigno, L., Laracca, M., Rasile, A.: An AMR-based three-phase current sensor for smart grid applications. IEEE Sens. J. 17(23), art. no. 7974752, 7704–7712 (2017)
Marek J.: Automotive MEMS sensors—trends and applications. In: International symposium on VLSI technology, systems and applications (VLSI-TSA). http://doi.org/10.1109/VTSA.2011.5872208
Liguori, C., Paciello, V., Paolillo, A., Pietrosanto, A., Sommella, P.: Characterization of motorcycle suspension systems: comfort and handling performance evaluation. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, pp. 444–449. ISBN: 978-146734622-1. https://doi.org/10.1109/i2mtc.2013.6555457
Carratù, M., Pietrosanto, A., Sommella, P., Paciello, V.: Suspension velocity prediction from acceleration measurement for two wheels vehicle. In: Proceedings of I2MTC 2017. https://doi.org/10.1109/i2mtc.2017.7969943
Liguori, C., Paciello, V., Paolillo, A., Pietrosanto, A., Sommella, P.: On road testing of control strategies for semi-Active suspensions. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, art. no. 6860931, pp. 1187–1192. ISBN: 978-146736385-3. https://doi.org/10.1109/i2mtc.2014.6860931
Liguori, C., Paciello, V., Paolillo, A., Pietrosanto, A., Sommella, P.: ISO/IEC/IEEE 21451 smart sensor network for the evaluation of motorcycle suspension systems. IEEE Sens. J. 15(5), 2549–2558. https://doi.org/10.1109/jsen.2014.2363945
Paciello, V., Sommella, P.: Smart sensing and smart material for smart automotive damping. IEEE Instrum. Measur. Mag. 16(5), art. no. 6616288, 24–30. https://doi.org/10.1109/mim.2013.6616288
Capriglione, D., Carratu’, M., Liguori, C., Paciello, V., Sommella, P.: A soft stroke sensor for motorcycle rear suspension. Measur. J. Int. Measur. Confed. 106(1), 46–52 (2017). https://doi.org/10.1016/j.measurement.2017.04.011
Capriglione, D., Carratu’, M., Pietrosanto, A., Sommella, P.: NARX ANN-based instrument fault detection in motorcycle. Measur. J. Int. Measure. Confed. 117, 304–311. https://doi.org/10.1016/j.measurement.2017.12.026
Catelani, M., Ciani, L.: A fault tolerant architecture to avoid the effects of Single Event Upset (SEU) in avionics applications. Measur. J. Int. Measur. Confed. 54, 256–263 (2014). https://doi.org/10.1109/i2mtc.2017.7969915
Leturiondo, U., Salgado, O., Ciani, L., Galar, D.: Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measur. J. Int. Measur. Confed. 108, 152–162 (2017). https://doi.org/10.1016/j.measurement.2017.02.003
Zhang, J., Yin, Z., Wang, R.: Nonlinear dynamic classification of momentary mental workload using physiological features and NARX-model-based least-squares support vector machines. IEEE Trans. Hum-Mach. Syst. 47(4), 536–549 (2017)
Spelta, C., Delvecchio, D., Savaresi, S.M.: A comfort oriented control strategy for semiactive suspensions based on half car model. In: Proceedings of ASME Conference DSCC20102, pp. 835–840 (2010)
Angrisani, L., Bonavolontà, F., Liccardo, A., Schiano Lo Moriello, R., Ferrigno,. L., Laracca, M., Miele, G.: Multi-channel simultaneous data acquisition through a compressive sampling-based approach. Measur. J. Int. Measur. Confed. 52(1), 156–172. https://doi.org/10.1016/j.measurement.2014.02.031
Capriglione, D., Liguori, T., Pietrosanto, A.: Real-time implementation of IFDIA scheme in automotive systems. IEEE Trans. Instrum. Meas. 56(3), 824–830 (2007). https://doi.org/10.1109/tim.2007.894899
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Capriglione, D., Carratù, M., Pietrosanto, A., Sommella, P. (2019). A Virtual ANN-Based Sensor for IFD in Two-Wheeled Vehicle. In: Andò, B., et al. Sensors. CNS 2018. Lecture Notes in Electrical Engineering, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-04324-7_55
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DOI: https://doi.org/10.1007/978-3-030-04324-7_55
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