A Robust Health Prognostics Technique for Failure Diagnosis and the Remaining Useful Lifetime Predictions of Bearings in Electric Motors

Magadán, L.; Suárez, Francisco J.; Granda, Juan C.; delaCalle, Francisco J.; Martínez, Daniel Fernando García

doi:10.3390/app13042220

Cited by 9 publications

(2 citation statements)

References 61 publications

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“…For instance, in (Magadán et al, 2023), an AE was trained using features extracted by considering interesting frequencies given by prior expert knowledge. Through this process, the AE was able to uncover health indicators from the lowdimensional latent space.…”

Section: Unsupervised Approachesmentioning

confidence: 99%

Unsupervised Physics-Informed Health Indicator Estimation for Complex Systems

Bajarunas,

Baptista,

Goebel

et al. 2023

PHM_CONF

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Developing Health Indicators (HI) is a crucial aspect of prognostics and health management of complex systems. Previous research has demonstrated the benefits of accurately determining the HI, which can lead to better performance of prognostic models. However, the existing methodologies for determining HI in complex systems are mostly semi-supervised and rely on assumptions that may not hold in real-world scenarios. The existing methods usually involve using a reference set of healthy sensor readings or run-to-failure data to infer HI. But the unsupervised inference of HI from sensor readings, which is challenging in scenarios where diverse operating conditions can mask the effect of degradation on sensor readings, has not been extensively researched. In this paper, we propose a novel physics-informed unsupervised model for determining HI. Unlike previous methods, constrained by assumptions, the proposed method uses prior general knowledge about degradation to infer HI, thereby eliminating the need for a reference set of healthy sensor readings. The proposed unsupervised model is an Autoencoder that incorporates constraints on its latent space to ensure consistency with knowledge about degradation. We assess the efficacy of the proposed model by analyzing a prevalent prognostic case study, specifically the turbofan engine dataset (N-CMAPSS). Our analysis considers the model's sensitivity to data availability and the resulting Health Index's quality, including trendability and monotonicity. Additionally, we investigate the impact of incorporating the Health Index in predicting Remaining Useful Life (RUL). We demonstrate that our proposed method generates a Health Index that exhibits greater monotonicity and trendability than the current state-of-the-art semi-supervised approach. Moreover, our approach for identifying the Health Index leads to enhanced prognostic performance compared to the existing semi-supervised approach.

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Section: Unsupervised Approachesmentioning

confidence: 99%

Unsupervised Physics-Informed Health Indicator Estimation for Complex Systems

Bajarunas,

Baptista,

Goebel

et al. 2023

PHM_CONF

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“…Depending on account for approximately 10%. The remaining faults are caused principally by looseness or gear faults [7], [15].…”

Section: Introductionmentioning

confidence: 99%

Early Fault Classification in Rotating Machinery With Limited Data Using TabPFN

Magadán,

Roldán-Gómez,

Granda

et al. 2023

IEEE Sensors J.

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Intelligent fault detection and classification is a cornerstone of prognostic and health management of rotating machinery research. Correctly classifying and predicting rotating machinery faults not only increases productivity in industrial plants, but also reduces maintenance costs. The datasets from real facilities needed to train fault classifiers often have few samples due to the expense of provoking faults in real scenarios to obtain data. This paper proposes the use of the Tabular Prior-Data Fitted Network (TabPFN) model for the classification of faults in rotating machinery. TabPFN is a model which has been pre-trained with a large amount of synthetic data with many causal relationships. This allows the model to perform Bayesian inference on the data used for training.The advantages of this model are its ability to be trained with limited data without generating overfitting problems and its high speed (if a GPU is available). To compare its performance with traditional algorithms for tabular classification such as XGboost and Random Forest, three public datasets were used. Results show that TabPFN performs more accurately than algorithms with limited data, so it is suitable to be deployed in real scenarios when the amount of data available from the monitored rotating machinery is limited.

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