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Time to failure prediction of rotating machinery using dynamic feature extraction and gaussian process regression

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Abstract

Recent advances in sensor technology and computing capabilities have enabled the creation of data-driven models that can support real-time decision making. Such a decision aid can allow for predictive maintenance (PdM) to be undertaken on a much greater scale in manufacturing plants. PdM includes data-driven prognostics and health management (PHM). A key element in developing prognostic models involves the acquisition of high-quality data, traditionally achieved through feature extraction methods to distill meaningful insights from extensive and noisy datasets. However, such methods may not handle noisy data well or address measurement errors adequately, potentially resulting in extracted features that inadequately represent the degradation process as a machine approaches failure or fault. Also, effects of sensor types on the feature extraction and prediction model have not been much explored yet. To overcome this limitation, we proposed a solution which involves dynamic feature extraction where a statistical penalty is introduced to mitigate the influence of noisy statistical features within a monotonic trend. Subsequently, the features extracted using this method are utilized to construct a health indicator (HI). Leveraging historical HI values, a probabilistic regression model may be used to forecast the time to failure (TTF) of rotating machinery with uncertainty propagation. To validate the proposed method, acceleration data were collected from rotating machinery for several run-to-failure cases. The proposed method is demonstrated to provide excellent forecasts of TTF for both accelerometer types.

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Notes

  1. When a machine failure occurs, many in the literature describe this as the "end of useful life." But, of course, in many cases the machine can be repaired to put it back into service life. For such a case, the authors prefer the phrase "time to failure (TTF)" over the frequently used "remaining useful life (RUL)." For elements that cannot be repaired, such as a bearing, RUL is certainly appropriate.

  2. In all, roughly 7000 h of data were collected on each of the three pumps using the two types of accelerometers. Every hour a sample was collected from each accelerometer (sampling rates of 12,000 Hz for piezo and 545 Hz for MEMS). These data were all stored and could be reviewed as necessary. In practice, the pumps were operated using a run-to-failure approach. When a failure occurred, the data leading up to the failure was analyzed. The data records in Fig. 5 do not show the entire "run-to-failure," but rather the portion of the signal in the days leading up to the failure, i.e., "function to failure."

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Acknowledgements

This work is supported by part of a project funded through the Wabash Heartland Innovation Network (WHIN) and the SMART Film consortium at Purdue University.

Funding

This work is supported by part of a project funded through the Wabash Heartland Innovation Network (WHIN) and the SMART Film consortium at Purdue University.

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  1. Laboratory for Sustainable Manufacturing, Environmental and Ecological Engineering, Purdue University, 500 Central Drive, West Lafayette, IN, USA

    Wo Jae Lee & John W. Sutherland

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  1. Wo Jae Lee
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Wo Jae Lee contributed to the conceptualization, methodology, experiment, software, and evaluation. John W. Sutherland contributed to the methodology, and reviewed and edited the paper.

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Correspondence to Wo Jae Lee.

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Lee, W.J., Sutherland, J.W. Time to failure prediction of rotating machinery using dynamic feature extraction and gaussian process regression. Int J Adv Manuf Technol 130, 2939–2955 (2024). https://doi.org/10.1007/s00170-023-12799-8

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