A novel Architecture for on-line Failure Prognosis using Probabilistic Least Squares Support Vector Regression Machines



Published Mar 26, 2021
Taimoor Khawaja Dr. George Vachtsevanos


The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic schemes (both model-based and data-driven) that attempt to forecast machinery health by constructing health propagation models for the underlying systems. In particular, algorithms that use the data-driven approach learn models directly from the data, rather than using a hand-built model based on human expertise. This paper introduces a novel architecture for data-driven Failure Prognosis of complex non-linear systems using Least Squares Support Vector Regression Machines (LSSVR). An adaptive recurrent LSSVR machine is proposed and augmented with a Bayesian Inference scheme that allows probabilistic estimates of future health deterioration. Extensions for efficient multi-step long-term prognostics and Remaining Useful Life (RUL) calculation are suggested. Data from a seeded fault test for a UH-60 planetary gearbox plate is used to test the online performance of the prognostics algorithm.

How to Cite

Khawaja, T., & Vachtsevanos, D. G. (2021). A novel Architecture for on-line Failure Prognosis using Probabilistic Least Squares Support Vector Regression Machines. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1391
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Bayesian reasoning, data driven prognostics, prognostics

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