An Intelligent Toolkit for Benchmarking Data-Driven Aerospace Prognostics

Abstract

Machine Learning (ML) has been largely employed to sensor data for predicting the Remaining Useful Life (RUL) of aircraft components with promising results. A review of the literature, however, has revealed a lack of consensus regarding evaluation metrics adopted, the state-of-the-art methods employed for performance comparison, the approaches to address data overfitting, and statistical tests to assess results' significance. These weaknesses in methodological approaches to experimental design, results evaluation, comparison and reporting of findings can result in misleading outcomes and ultimately produce less effective predictors. Arbitrary choices of approaches for novel method's evaluation, the potential bias that can be introduced, and the lack of systematic replication and comparison of outcomes might affect the findings reported and misguide future research. For further advances in this area, there is therefore an urgent need for appropriate benchmarking methodologies to assist evaluating novel methods and to produce fair performance rankings. In this paper we introduce an opensource, extensible benchmarking library to address this gap in aerospace prognosis. The library will assist researchers to conduct a proper and fair evaluation of their novel ML RUL predictive models. In addition, it will assist stimulating better practices and a more rigorous experimental design approach across the field. Our library contains 13 state-of-the-art ML methods, 12 metrics for algorithm performance evaluation and tests for statistical significance. To demonstrate the library's functionalities, we apply it to gas turbine engine prognostic datasets.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

“…The performance of the new approach is examined by observing deep learning models' predictive performance for two case studies: (1) Gas turbine engine remaining useful life (RUL) prediction using commercial modular aero-propulsion system simulation (CMAPSS) with the weighted loss function proposed in this paper and (2) air pressure system (APS) fault detection in trucks using the FL. CMAPSS is a run-to-failure gas turbine engine dataset openly sourced by NASA [12] and it is the standard dataset to compare different machine learning models for aerospace prognostics [13,14]. The APS fault detection dataset is collected from heavy Scania trucks in everyday usage.…”