Machine condition monitoring (MCM) plays a pivotal role in ensuring the reliability, safety, and efficiency of a production and operation system. Fault feature extraction (FFE), as an important step within MCM, aims to filter out interference components (ICs) and extract fault components (FCs) from raw signals. Consequently, it facilitates incipient fault detection and performance degradation assessment. A recently proposed optimized weights spectrum (OWS) provided a data-driven FFE methodology with a strong theoretical foundation, but it was found that the OWS is highly sensitive to a frequency fluctuation problem caused by rotational speed fluctuations and sensor errors, which may result in fake fault signatures. Therefore, a robust optimized weights spectrum (ROWS) is proposed in this paper to solve the frequency fluctuation problem. The ROWS is inspired by an intuitive idea that employs functions with adaptive frequency bandwidths instead of isolated spectral lines to represent frequency components, thus mitigating the impact of the frequency fluctuation problem. To adaptively determine the optimal parameters for these functions and estimate the corresponding ROWS, an alternative optimization strategy is adopted. Then, the proposed ROWS can be obtained when convergence conditions of this strategy are satisfied. Finally, the effectiveness and superiority of the proposed ROWS are verified by three real-world cases, i.e., the proposed ROWS can solve the frequency fluctuation problem and provide robust results for interpretable FFE.

中文翻译：

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鲁棒优化权重谱：通过解决频率波动问题增强可解释故障特征提取方法


机器状态监测（MCM）在确保生产和运营系统的可靠性、安全性和效率方面发挥着关键作用。故障特征提取（FFE）是MCM中的一个重要步骤，旨在滤除干扰成分（IC）并从原始信号中提取故障成分（FC）。因此，它有利于早期故障检测和性能退化评估。最近提出的优化权谱（OWS）为数据驱动的FFE方法提供了坚实的理论基础，但研究发现OWS对转速波动和传感器误差引起的频率波动问题高度敏感，这可能会导致虚假故障签名。因此，本文提出鲁棒优化权重谱（ROWS）来解决频率波动问题。 ROWS的灵感来自于一个直观的想法，即使用具有自适应频率带宽的函数而不是孤立的谱线来表示频率分量，从而减轻频率波动问题的影响。为了自适应地确定这些函数的最佳参数并估计相应的 ROWS，采用了另一种优化策略。然后，当满足该策略的收敛条件时，可以获得所提出的ROWS。最后，通过三个实际案例验证了所提出的ROWS的有效性和优越性，即所提出的ROWS可以解决频率波动问题并为可解释的FFE提供稳健的结果。