Predictive Maintenance of Aircraft Braking Systems: A Machine Learning Approach to Clustering Brake Wear Patterns

Le résumé fourni par la source

The operational integrity and performance of aircraft braking systems are paramount to commercial aircraft safety and maintenance planning efficiency. Operational integrity refers to the reliability of the braking systems under various conditions, including the ability to withstand high temperatures and resist wear. High operational integrity means fewer unexpected failures, which leads to improved overall aircraft safety and reduced unplanned maintenance. Brake performance includes the effectiveness of braking during landing and taxiing as well as the system's responsiveness to pilot inputs. This study presents a thorough approach to understanding carbon brake pad degradation by benchmarking a suite of unsupervised Machine Learning (ML) clustering algorithms. The objective is to uncover distinct wear patterns and identify salient features differentiating varying degrees of wear. This effort leverages data that includes aircraft-specific parameters (such as aircraft weight), operational conditions (such as flight duration), and environmental factors (such as static air temperature) along with airport characteristics (such as runway length) observed across an airline's fleet of widebody aircraft variants. A Random Forest classifier is implemented to determine the most influential predictors of wear levels, providing a robust feature importance analysis. Methods including Principal Component Analysis (PCA) and an autoencoder are then leveraged to further reduce the dimensionality of the dataset. Various clustering techniques, including K-Means and Agglomerative Clustering, are considered and benchmarked with varying hyperparameter settings. These methods are applied without the knowledge of pre-assigned wear labels, ensuring an unbiased grouping based on intrinsic data characteristics. The performance of these algorithms is then quantitatively assessed using unsupervised evaluation metrics (e.g., Silhouette score) and supervised metrics (e.g., Rand Index) to gain insights from wear labels derived from the available wear pin parameter from the aircraft data. Categorical labels (i.e., High, Medium, or Low wear) are created by interpolating the wear pin signal and categorizing the degradation per flight into quantiles. The top features identified by Random Forest are then analyzed for differences across clusters. This iterative clustering process helps explain the data's intrinsic structure, revealing the foremost features indicative of brake wear. The findings have the potential to contribute to predictive maintenance strategies by enhancing the understanding of how various operating and environmental conditions impact carbon brake pad degradation.

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