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Article summary:
1. Vibration signal analysis is commonly used for fault diagnosis of rotating machinery.
2. Rotating machinery vibration signals are often dominated by the rotating frequency and its integer or fractional harmonics, which contain health information of the machinery.
3. The Multiple Order Probabilities Approach (MOPA) has been proposed to track instantaneous angular speed and avoid interferences from speed-irrelative components and background noise.
Article analysis:
The article provides a comprehensive overview of the use of adaptive high-resolution order spectrum for complex signal analysis of rotating machinery, including its principles and applications. The article is well-structured and clearly written, making it easy to understand the concepts discussed in the article. The authors provide a detailed description of the challenges posed by nonstationary conditions in vibration feature extraction, as well as an overview of existing methods for extracting frequency components from vibration signals. Furthermore, they discuss the drawbacks associated with conventional order spectrum methods, such as interference from speed-irrelative components and background noise, spectral blur due to nonstationarity of amplitude envelope, and difficulty in installing rotary encoders or tachometers on running machines to measure instantaneous speed.
The authors then introduce the Multiple Order Probabilities Approach (MOPA) as a solution to these problems, providing examples of how it has been applied in various contexts such as wind turbine gearboxes and rotors. They also provide a brief overview of other methods for estimating instantaneous speed directly from vibration signals without external instruments.
In terms of trustworthiness and reliability, this article appears to be unbiased and presents both sides equally; however there are some potential biases that should be noted. For example, while the authors do mention other methods for estimating instantaneous speed directly from vibration signals without external instruments, they focus mainly on MOPA which may lead readers to believe that MOPA is superior to other methods when this may not necessarily be true in all cases. Additionally, while the authors provide examples of how MOPA has been applied in various contexts such as wind turbine gearboxes and rotors, they do not provide any evidence or data to support their claims about its effectiveness or accuracy in these contexts which could have strengthened their argument further.
In conclusion, this article provides a comprehensive overview of adaptive high-resolution order spectrum for complex signal analysis of rotating machinery with clear explanations throughout; however there are some potential biases that should be noted when considering its trustworthiness and reliability.