An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment



David Siegel Jay Lee


This paper presents a health assessment methodology, as well as specific residual processing and figure of merit algorithms for anemometers in two different configurations. The methodology and algorithms are applied to data sets provided by the Prognostics and Health Management Society 2011 Data Challenge. The two configurations consist of the “paired” data set in which two anemometers are positioned at the same height, and the “shear” data set which includes an array of anemometers at different heights. Various wind speed statistics, wind direction, and ambient
temperature information are provided, in which the objective is to classify the anemometer health status during a set of samples from a 5 day period. The proposed health assessment methodology consists of a set of data processing steps that include: data filtering and pre-processing, a residual or difference calculation, and a k-means clustering based figure of merit calculation. The residual processing for the paired data set was performed using a straightforward difference calculation, while the shear data set utilized an additional set of algorithm processing steps to calculate a weighted residual value for each anemometer. The residual processing algorithm for the shear data set used a set of auto-associative neural network models to learn the underlying correlation relationship between the anemometer sensors and to calculate a weighted residual value for each of the anemometer wind speed measurements. A figure of merit value based on the mean value of the smaller of the two clusters for the wind speed residual is used to determine the health status of each anemometer. Overall, the proposed methodology and algorithms show promise, in that the results from this approach resulted in the top score for the PHM 2011 Data Challenge Competition. Using different clustering algorithms or density estimation methods for the figure of merit calculation is being considered for future work.

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Auto-Associative Neural Network, K-means Clustering, Anemometer, Sensor Fault Detection

Antory, D., Kruger, U., Irwin, G.W., & McCullough, G. (2005). Fault Diagnosis in Internal Combustion Engines using Nonlinear Multivariate Statistics. Proceedings of IME-Part I: Journal of Systems and
Controls Engineering, vol. 219, pp. 243-258. doi: 10.1243/095965105X9614
Beltran, J., Llombart, A., & Guerrero, J.J. (2009). Detection of Nacelle Anemometers Faults in a Wind Farm. Proceedings of the International Conference on Renewable Energies and Power Quality, April 15-17, Valencia, Spain.
Capriglione, D., Liguroi, C., Pianese, C., & Pietrosanto, A. (2003). On-line Sensor Fault Detection, Isolation, and Accommodation in Automotive Engines. IEEE Transactions on Instrumentation and Measurement, vol. 52, pp. 1182-1189. doi: 10.1109/TIM.2003.815994
Clark, S.H., Clay, O., Goglia, J.A., Hoopes,T.R., Jacobs, L.T., Smith, R.P. (2009). Investigation of the NRG # 40 Anemometer Slowdown, Proceedings of the AWEA Windpower Conference and Exhibition, May 4-7, Chicago, IL.
Hale, E., Fusina, L., & Brower, M. (2011). Correction factors for NRG #40 Anemometers Potentially affected by Dry Friction Whip: Characterization, Analysis, and Validation. Wind Energy, doi: 10.1002/we.476
Hartigan, J.A., & Wong, M.A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, pp. 100-108.
Hines, J.W., & Garvey, R.D. (2006). Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection. Journal of Pattern Recognition Research, vol. 1, pp. 2-15.
Hsu, S.A., Meindl, E.A., & Gilhousen, D.B. (1994). Determining the Power-Law Wind Profile Exponent under Near-Neutral Stability Conditions at Sea. Journal of Applied Meteorology, vol. 33, pp. 757-772.
Hu, X., Qiu, H., & Iyer, N. (2007). Multivariate change detection for time series data in aircraft engine fault diagnostics. IEEE Conference on Systems, Man, and Cybernetics, October 7-10, Montreal, Quebec. Doi: 10.1109/ICSMC.2007.4414131
Jain, A.K., Murty, M.N., & Flynn, P.J. (1999). Data Clustering: A Review. ACM Computing Surveys, vol. 31, pp. 264-323.
Kramer, M.A. (1991). Nonlinear Principal Component Analysis using Autoassociative Neural Networks. AIChe Journal, vol. 37, pp. 233-243. doi: 10.1002/aic.690370209
Kusiak, A., Zheng, H., & Zhang, Z. (2011). Virtual Wind Speed Sensor for Wind Turbines. Journal of Energy Engineering, vol. 137, pp. 59-69. doi: 10.1061/(ASCE)EY.1943-7897.0000035
Mohandes, M.A., Rehman, S., & Halawani, T.O. (1998). A Neural Networks Approach for Wind Speed Prediction. Renewable Energy, vol. 13, pp. 345-354. doi: 10.1016/S0960-1481(98)00001-9
Murakami, S., Mochida, A., & Kato, S. (2003). Development of Local Area Wind Prediction System for Selecting Suitable Site for Windmill. Journal of Wind Energy and Industrial Aerodynamics, vol. 22, pp. 679-688. doi: 10.1016/j.jweia.2003.09.040
Patton, R.J. (1991). Fault detection and diagnosis in aerospace systems using analytical redundancy, Computers & Control Engineering Journal, vol. 2, pp. 127-136.
Petersen, E.L., Mortensen, N.G., Landberg, L., Hujstrup, J., & Frank, H.P. (1998). Wind Power Meteorology Part II: Siting and Models. Wind Energy, vol. 1, pp. 55-72.
Peterson, E.W., & Hennessey Jr., J.P. (1977). On the Use of Power Laws for Estimates of Wind Power Potential. Journal of Applied Meteorology, vol. 17, pp. 390-394.
PHM Society 2011 Data challenge Competition, (2011). []
Pollard, D. (1981). Strong Consistency of K-Means Clustering. The Annals of Statistics, vol. 9, pp. 135-140. doi: 10.1214/aos/1176345339
Schwabacher, M. (2005). A Survey of Data Driven Prognostics. In AIAA Infotech@ Aerospace Conference, September 26-29, Arlington, VA.
Thissen, U., Melssen, W.J., & Buydens, L.M.C. (2001). Nonlinear Process Monitoring using bottle-neck neural networks. Analytica Chimica Acta, vol. 446, pp. 371-383. doi:10.1016/S0003-2670(01)01266-1
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S.N. (2003). A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-Based Methods. Computers and Chemical Engineering, vol. 27, pp. 293-311. doi: 10.1016/S0098-1354(02)00160-6
Xu, X., Hines, J.W., & Uhrig, R.E. (1999). Sensor Validation and Fault Detection using Neural Networks. In Proceedings of the Maintenance and Reliability Conference, May 10-12, Gatlinburg, TN.
Technical Papers