International Journal of Prognostics and Health Management

Vibration-based Data-driven Fault Diagnosis of Rotating Machines Operating Under Varying Working Conditions

David Latil, Raymond Houé Ngouna, Kamal MEDJAHER, Stéphane LHUISSET — Tue, 01 Jul 2025 00:00:00 +0000

The intelligent fault diagnosis of rotating machines has been significantly advanced by learning-based techniques in recent years. However, the performance of these techniques can drastically decrease under varying working conditions (VWC). This paper investigates the root causes of these decreased capabilities by analyzing the impact of VWC on each of the key steps in intelligent fault diagnosis for rotating machines. In addition, techniques proposed in the literature to mitigate these effects are reviewed and assessed for their relevance in industrial applications. A bibliometric study is also conducted to understand the evolution of research in this field over the past two decades. Beyond providing a synthesis of the existing literature, this review is intended for researchers, engineers, and industry professionals seeking to implement robust fault diagnosis systems under varying operational conditions. It offers insights on when and how these techniques can be effectively applied, depending on specific industrial scenarios and assumptions.

LSTM and Transformers based methods for Remaining Useful Life Prediction considering Censored Data

Jean-Pierre Noot, Mikaël Martin, Etienne Birmele — Fri, 04 Jul 2025 00:00:00 +0000

Predictive maintenance deals with the timely replacement of industrial components relatively to their failure. It allows to prevent shutdowns as in reactive maintenance and reduces the costs compared to preventive maintenance. As a consequence, Remaining Useful Life (RUL) prediction of industrial components has become a key challenge for condition-based monitoring. In many applications, in particular those for which preventive maintenance is the general rule, the prediction problem is made harder by the rarity of failing instances. Indeed, the interruption of data acquisition before the occurrence of the event of interest leads to right censored data. Recent deep-learning architectures, that show the best results of the literature for complete-life data, most often do not consider censoring, even though its rate in the industrial environment may be high.
The present article introduces a method which considers censored data for the Dual Aspect Self-Attention based on a Transformer proposed by (Z. Zhang, Song, & Li, 2022), and puts it into competition a modified version of the ordinal regression-based LSTMof (Vishnu, Malhotra, Vig, & Shroff, 2019). The evaluation of the resulting method on the CMAPSS and N-CMAPSS benchmark dataset shows that it is competitive compared to the state-of-the-art RUL prediction methods for a low censoring rate and more efficient for a high rate of censoring in large enough data sets. Finally, conformal prediction is used to estimate confidence intervals for the predictions.

Remaining Useful Life Prediction Using Attention-LSTM Neural Network of Aircraft Engines

Marouane Dida, Abdelhakim Cheriet, Mourad Belhadj — Fri, 04 Jul 2025 00:00:00 +0000

Accurate prediction of the Remaining Useful Life (RUL) is essential for the effective implementation of Prognostics and Health Management (PHM) in aerospace, particularly in enhancing aero-engine reliability and forecasting potential failures to reduce maintenance costs and human-related risks.

The NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, utilized in the 2021 PHM Data Challenge, serves as a widely recognized open-source benchmark, providing simulated turbofan engine data collected under realistic flight conditions. Previous deep learning approaches have leveraged this dataset to predict the remaining useful life of engine units.

However, data-driven methods for RUL prediction in aerospace often encounter challenges such as high model complexity, limited prediction accuracy, and reduced interpretability. To address these issues, this paper presents a novel hybrid framework that incorporates an attention mechanism to enhance aircraft engine RUL prognostics. Specifically, we employ a self-attention mechanism to effectively capture relationships and interactions among different features, enabling the transformation of high-dimensional feature spaces into lower-dimensional representations.

The proposed model, which integrates an LSTM network, demonstrates superior performance in predicting turbofan engine RUL. Experimental results validate its effectiveness, achieving RMSE values of 12.33 and 11.76, along with score values of 200 and 212 on the FD001 and FD003 sub-datasets, respectively. These results surpass those of other state-of-the-art methods on the C-MAPSS dataset.