Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque



Published Mar 5, 2024
Roberto Diversi Nicolò Speciale Matteo Barbieri


This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.

Abstract 99 | PDF Downloads 108



Condition monitoring, Electric-cam mechanisms, Programmable Logic Controllers, Wavelets, Autoregressive models

Barbieri, M., Bosso, A., Conficoni, C., Diversi, R., Sartini, M., & Tilli, A. (2018). An onboard model-of-signals approach for condition monitoring in automatic machines. In Enterprise interoperability: Smart services and business impact of enterprise interoperability (pp. 263–269). Wiley – ISTE.
Barbieri, M., Diversi, R., & Tilli, A. (2019). Condition monitoring of ball bearings using estimated AR models as logistic regression features. In 2019 18th European Control Conference (ECC 2019) (pp. 3904–3909).
Barbieri, M., Diversi, R., & Tilli, A. (2020). Condition monitoring of electric-cam mechanisms based on model-of-signals of the drive current higher-order differences. IFAC-PapersOnLine, 53(2), 802-807. (21st IFAC World Congress)
Barbieri, M., Nguyen, K. T. P., Diversi, R., Medjaher, K., & Tilli, A. (2021). RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques. IFACPapersOnLine, 32, 1421–1440.
Basseville, M. (1988). Detecting changes in signals and systems— a survey. Automatica, 24(3), 309-326.
Biagiotti, L., & Melchiorri, C. (2008). Trajectory planning for automatic machines and robots. Springer Science & Business Media.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Cerrada, M., S´anchez, R.-V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J. V., & V´asquez, R. E. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169–196.
Chai, N., Yang, M., Ni, Q., & Xu, D. (2018). Gear fault diagnosis based on dual parameter optimized resonance-based sparse signal decomposition of motor current. IEEE Transactions on Industry Applications, 54(4), 3782-3792.
Daubechies, I. (1992). Ten lectures on wavelets. SIAM. Foundation, P. (1992). Plcopen foundation. Retrieved from (Accessed: 2023-11-01)
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
Grivel, E., Diversi, R., & Merchan, F. (2021). Kullback-Leibler and R´enyi divergence rate for Gaussian stationary ARMA processes comparison. Digital Signal Processing, 116, 103089.
Isermann, R. (2005). Model-based fault-detection and diagnosis–status and applications. Annual Reviews in control, 29(1), 71–85.
Isermann, R. (2006). Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483–1510.
Kar, C., & Mohanty, A. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, 20(1), 158-187.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems – Reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314–334.
Ljung, L. (1999). System identification: theory for the user. Prentice-hall.
Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors -— A review. IEEE transactions on energy conversion, 20(4), 719–729.
Peng, Z., & Chu, F. (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 18(2), 199 – 221.
Pincus, S. (1995). Approximate entropy (ApEn) as a complexity measure. Chaos An Interdisciplinary Journal of Nonlinear Science, 5(1), 110-117.
Qi, R., Zhang, J., & Spencer, K. (2023). A review on data-driven condition monitoring of industrial equipment. Algorithms, 16(1).
Singh, S., & Kumar, N. (2017). Detection of bearing faults in mechanical systems using stator current monitoring. IEEE Transactions on Industrial Informatics, 13(3), 1341-1349.
Söderström, T., & Stoica, P. (1989). System identification. Prentice Hall.
Soualhi, A., Hawwari, Y., Medjaher, K., Clerc, G., Hubert, R., & Guillet, F. (2017). Prognostics and health management for maintenance practitioners - review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8(3).
Soualhi, A., Hawwari, Y., Medjaher, K., Clerc, G., Hubert, R., & Guillet, F. (2018). Phm survey: Implementation of signal processing methods for monitoring bearings and gearboxes. International Journal of Prognostics and Health Management, 9(2).
Strang, G., & Nguyen, T. (1996). Wavelets and filter banks. Wellesley-Cambridge Press.
Wei, B., & Gibson, J. (2000). Comparison of distance measures in discrete spectral modeling. In Proc. of the 9th Digital Signal Processing Workshop.
Yan, R., Gao, R. X., & Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1-15.
Technical Papers