Integrated Vehicle Health Management Centre, Cranfield University, Cranfield MK43 0AL, UK.
Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK.
Sensors (Basel). 2023 Jan 15;23(2):1005. doi: 10.3390/s23021005.
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.
旋转机械的故障诊断对于防止机器停机非常重要，为基于状态的维护 (CBM) 决策提供了可验证的支持。由于可以自动提取和选择特征，基于深度学习的故障诊断操作变得越来越流行。然而，对于这些模型来说，在不同尺度的旋转机械部件、不同旋转部件的单一和多种故障、不同的运行速度和不同的负载条件下，要取得优异的结果是具有挑战性的。为了解决这些挑战，本文提出了一种全面的学习方法，该方法具有优化的信号处理变换，可用于诊断不同旋转机械部件（齿轮箱、轴承和轴）的单一和多种故障。针对这些部件的多种故障诊断，探讨了优化的双相干、谱峭度和循环谱相干特征空间以及深度混合集成学习。通过在包含来自三个公共存储库的多种故障的复合数据集上对整个框架进行单一联合训练，对所提出方法的性能进行了分析。与使用这些数据集的最新方法进行比较的结果表明，我们的方法在不同的部件和故障下进行名义上的重新训练时，都能得到更好的结果。
