A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms



Published Nov 17, 2020
Francesco Cannarile Piero Baraldi Pierluigi Colombo Enrico Zio


Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

Abstract 30 | PDF Downloads 19



sensor validation, Image Pattern Recognition, scalogram analysis, Continuous Wavelet Transforms (CWT)

Baraldi, P., Di Maio, F., Turati, P., Zio, E. (2015). Robust signal reconstruction for condition monitoring of industrial components via a modified Auto Associative Kernel Regression method. Mechanical Systems and Signal Processing, 60, pp. 29-44.
Baraldi, P., Cannarile, F., Di Maio, F., Zio, E. (2016). Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Engineering Applications of Artificial Intelligence, 56, pp. 1-13.
Baraldi, P., Gola, G., Zio, E., Roverso, D., Hoffmann, M. (2011). A randomized model ensemble approach for reconstructing signals from faulty sensors. Expert Systems with Applications, 38 (8), pp. 9211-9224.
Baraldi, P., Mangili, F., Gola, G., Nystad, B.H., Zio, E. (2014). A hybrid ensemble-based approach for process parameter estimation and degradation assessment in offshore oil platforms. International Journal of Performability Engineering, 10 (5), pp. 497-509.
Batuwita, R. and Palade, V. (2013). Class Imbalance Learning Methods for Support Vector Machines. In Imbalanced Learning: Foundations. Algorithms and Applications. John Wiley & Sons.
Cannarile, F., Baraldi, P., Compare, M., Zio, E. (2017). An unsupervised clustering method for assessing the degradation state of cutting tools in the packaging industry. Safety and Reliability: Theory and Application-Proceedings of the European Safety and Reliability Conference, ESREL 2017.
Coble, J.B., Meyer, R.M., Ramuhalli, P., Bond, L.J., Hashemian, H., Shumaker, B., et al. (2012). A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants. Pacific Northwest National Laboratory.
Gao, R. X., Yan, R., (2011). Wavelets: Theory and Applications for Manufacturing. New York: Springer.
Garvey, J., Garvey, D., Seibert, R., Hines, J.W. (2007). Validation of on-line monitoring techniques to nuclear plant data. Nuclear Engineering Technologies, 39 (2), pp. 149-158.
Gross, K.C., Singer, R.M., Wegerich, S.W., Herzog, J.P. (1997). Application of a model-based fault detection system to nuclear plant signals. Proceedings of the intelligent system applications to power systems, ISAP, Seoul, Korea, pp 66–70.
Hines, J.W., Uhrig, R.E., Wrest, D.J. (1998). Use of autoassociative neural networks for signal validation. Journal of Intelligent and Robotic System, 21, pp. 143-154.
Holschneider, M., Tchamitchian, P. (1989). Regularite locale de la fonction non-differentiable de Riemann. Les ondelettes. Lecture notes in Mathematics. NewYork: Springer-Verlag.
Karacan, C.O., Olea, R.A. (2014). Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation. Journal of Applied Geophysics, 105, pp. 147-158.
Kovačević, J., Chebira, A. (2007). Life beyond bases: The advent of frames (Part I). IEEE Signal Processing Magazine, 24 (4), pp. 86-104.
Lee, J.-M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.-B. (2004). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59 (1), pp. 223-234.
Li, H. (2010). Gear Fault Diagnosis Based on Continuous Morlet Wavelet Amplitude and PhaseMap. Seventh International Conference on Fuzzy Systems and Knowledge Discovery 2010, 6, pp. 2619-2622.
Mallat, S., (2008). A Wavelet Tour of Signal Processing. Academic Press.
Mallat, S., Hwang, W.L. (1992). Singularity detection and processing with wavelets. IEEE Transactions on Information Theory, 38 (2), pp. 617-643.
Miao, Q., Huang, H.-Z., Fan, X. (2007). Singularity detection in machinery health monitoring using Lipschitz exponent function. Journal of Mechanical Science and Technology, 21 (5), pp. 737-744.
Ni, K., Ramanathan, N., Chehade, M.N.H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., Srivastava, M. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks, 5 (3), pp. 1-29.
Penha, R., Hines, J. (2001). Using principal component analysis modeling to monitor temperature sensors in a nuclear research reactor. Maintenance and reliability conference (MARCON 2001).
Qiu, H., Lee, J., Lin, J., Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, Volume 289, (4-5), pp. 1066-1090.
Rasmussen, B., Hines, J.W., Uhrig, R.E. (2000). Nonlinear partial least squares modeling for instrument surveillance and calibration verification. Proceedings of the maintenance and reliability conference (MARCON 2000).
Roverso, D., Hoffmann, M., Zio, E., Baraldi, P., Gola, G. (2007). Solutions for plant-wide on-line calibration monitoring. Proceedings of the European Safety and Reliability Conference 2007, ESREL 2007 - Risk, Reliability and Societal Safety.
Sengüler, T., Seker, S. (2016). Continuous wavelet transform for ferroresonance detection in power systems. Electrical Engineering, 99 (2), pp. 595-600.
Sharma, A.B., Golubchik, L., Govindan, R. (2010). Sensor faults: Detection methods and prevalence in real-world datasets. ACM Transactions on Sensor Networks, 6 (3), art. no. 23.
Struzik, Z.R. (2001). Wavelet methods in (financial) timeseries processing. Physica A: Statistical Mechanics and its Applications, 296 (1-2), pp. 307-319.
Tolle, G., Polastre, J., Szewczyk, R., Culler, D., Turner, N., Tu, K., Burgess, S., Dawson, T., Buonadonna, P., Gay, D., and Hong, W. (2005). A Macroscope in the Redwoods. Proceedings of the 2nd international conference on Embedded networked sensor systems (SenSys), pp. 51–63.
Torrence, C., Compo, G.P. (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79 (1), pp. 61-78.
Tu, C.-L., Hwang, W.-L., Ho, J. (2005). Analysis of singularities from modulus maxima of complex wavelets. IEEE Transactions on Information Theory, 51 (3), pp. 1049-1062.
Wegener, I., (2005). Complexity theory: Exploring the limits of efficient algorithms. Complexity Theory: Exploring the Limits of Efficient Algorithms, pp. 1-308.
Technical Papers