Event detection in a noisy time series data using static smoothening and gradient variation analytics



Published Sep 4, 2023
Saravanaram T Unnat Mankad Simi Madhavan Karatha Carmine Allegorico Arati Halaki


Data-driven prognostic industrial asset health management is essential to improve reliability and availability of industrial machineries. Industrial sensors capture multiple phenomena of underlying assets like environment impact, system degradation, process variation, instrument noise, control system response or user induced actions. Some of these phenomena have distinct signature and have impact on component life and remaining useful life. Capturing these events’ signature help to apply advanced AI algorithm to categorize various failure modes and early detection. For Ex: Some of transient events can contribute to thermal cycling of component and in turn reduces life of high temperature and low thermal mass components. Due to random and nonstandard nature of these events, it is extremely challenging to detect and extract these events. Existing change point detection algorithms have limitations to detect sudden variations, which is common due to process or control actions. The noisy signal adds additional challenge to differentiate between important event and noise. In this work of time series analysis, we propose a new approach for consistent estimation of numbers and locations of the change-points. With this tunable algorithm combined with event labelling and pattern search, we can detect events of our functional needs and use them as a feature for our prediction models. This methodology has opened exciting opportunity to further analyse these events with development of classification system and time to fail prediction models and also apply large language models for time series data.  

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timeseries, event search, event detection, change point detection, minor cycle, IOT data

Allegorico, C., & Mantini, V. (2014). A data-driven approach for on-line gas turbine combustion monitoring using classification models. In Phm society european conference (Vol. 2).

Cheremisinoff, N. P. (2001). Condensed encyclopedia of polymer engineering terms. Butterworth-Heinemann.

Iannitelli, M., Allegorico, C., Garau, F., & Capanni, M. (2018). A hybrid model for on-line detection of gas turbine lean blowout events. In Phm society european conference (Vol. 4).

Johnson, P., Moriarty, J., & Peskir, G. (2017). Detecting changes in real-time data: a user’s guide to optimal detection. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2100), 20160298.

Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.

Neyman, J., & Pearson, E. S. (1933). Ix. on the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231(694-706), 289–337.

Roberts, S. (2000). Control chart tests based on geometric moving averages. Technometrics, 42(1), 97–101.

Rupture python package. (n.d.).

Silva, A., Zarzo, A., Gonzalez, J. M. M., & Munoz-Guijosa, ́ J. M. (2020). Early fault detection of single-point rub in gas turbines with accelerometers on the casing based on continuous wavelet transform. Journal of sound and vibration, 487, 115628.

Sofronov, G., Polushina, T., & Priyadarshana, M. (2012). Sequential change-point detection via the cross-entropy method. In 11th symposium on neural network applications in electrical engineering (pp. 185–188).

Szczepankowski, A., & Przysowa, R. (2022). Thermal degradation of turbine components in a military turbofan. Engineering Failure Analysis, 134, 106088.
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