Rolling bearings are a vital component for transmitting and supporting in rotating machinery. They are susceptible to failure since their operation under high speeds and heavy loads. Bearing failure may disrupt the manufacturing process and cause catastrophic accidents. Therefore, condition monitoring for bearings is essential to minimize operational disruptions and avoid unforeseen casualties. High-frequency resonance demodulation technology (HFRDT) and many improved approaches provide an effective rolling bearing’s fault diagnosis tool. Rotating machinery tends to be sophisticated in Industry 4.0, while workplace interference escalates. As a result, fault information and interference components are coupled within the same frequency band range, rendering linear filter HFRDT-based methods ineffective. Therefore, spectral amplitude modulation (SAM) is proposed to nonlinearly enhance the fault information for bearing diagnosis. However, interference components dominate the monitor signal in complex environments, making it challenging for SAM to achieve a satisfactory result in practical applications. Therefore, an adaptive residual spectral amplitude modulation (ARSAM) approach is proposed for diagnosing bearing under complex interference environments. In this work, the raw signal is separated into multiple narrowband signals within various frequency bands. Then, residual spectral amplitude modulation (RSAM) is performed on the narrowband signal to obtain fault information nonlinearly. Subsequently, a different fault harmonic-to-noise rate (DFHNR) index is presented to select the optimal signal with rich fault information adaptively. Lastly, the simulated signal and real engineering data are analyzed to showcase the performance of the ARSAM. The results indicate the superior performance of the proposed approach in recognizing bearing faults under complex working environments.

中文翻译：

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自适应残余谱幅度调制：复杂干扰环境下轴承诊断的新方法


滚动轴承是旋转机械中传递和支撑的重要部件。由于它们在高速和重负载下运行，因此很容易出现故障。轴承故障可能会扰乱制造过程并导致灾难性事故。因此，轴承状态监测对于最大限度地减少运行中断并避免意外伤亡至关重要。高频谐振解调技术（HFRDT）和许多改进方法提供了有效的滚动轴承故障诊断工具。工业 4.0 中的旋转机械趋于复杂，而工作场所的干扰也不断升级。因此，故障信息和干扰分量在同一频带范围内耦合，导致基于线性滤波器 HFRDT 的方法无效。因此，提出频谱幅度调制（SAM）来非线性增强轴承诊断的故障信息。然而，在复杂环境下，干扰成分在监测信号中占主导地位，这使得SAM在实际应用中很难获得满意的结果。因此，提出了一种自适应剩余频谱幅度调制（ARSAM）方法来诊断复杂干扰环境下的方位。在这项工作中，原始信号被分成不同频带内的多个窄带信号。然后，对窄带信号进行残余频谱幅度调制（RSAM），非线性地获取故障信息。随后，提出了不同的故障谐波噪声比（DFHNR）指标来自适应地选择具有丰富故障信息的最佳信号。最后，对模拟信号和实际工程数据进行分析，以展示 ARSAM 的性能。 结果表明该方法在复杂工作环境下识别轴承故障方面具有优越的性能。