Choosing the right signal processing tools for mechanical systems

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL Mechanical Systems and Signal Processing Pub Date : 2025-02-01 DOI:10.1016/j.ymssp.2024.112090
Robert B. Randall , Jérôme Antoni
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Abstract

Simon Braun was one of the first to recognise the special requirements for processing of signals from mechanical systems, which was why he launched the journal Mechanical Systems and Signal Processing. Mechanical engineers are typically not well trained in signal processing, so signal processing specialists are often recruited from other areas, e.g. electrical engineering, speech processing, acoustics, which often have different requirements. A very important application area of signal processing in mechanical engineering (and mechatronics) is robotics and active control. This requires causal processing in real-time, but that places restrictions on the results, since causal filters have poor characteristics and phase distortion. There are also problems with differentiation, integration, and Hilbert transformation when performed directly in the time domain. Machine Condition Monitoring is another very important area of signal processing for mechanical engineers. This paper shows that causal signal processing is not only not required for Machine Condition Monitoring, even for online monitoring of critical machines, but gives problems and distortions that can be avoided with non-causal signal processing. The paper illustrates the advantages gained using non-causal processing, mostly based on FFT (fast Fourier transform) analysis, for ideal filtration with zero phase shift, as well as error-free differentiation/integration and Hilbert transformation via the frequency domain. However, the circularity of the (non-causal) FFT algorithm gives “wraparound effects”, which must be mitigated. The paper has a short discussion of the situations, apart from active control, where causal processing is of advantage, such as octave-based filtration, and real-time zoom as a precursor to FFT analysis. Finally, the paper discusses the special requirements of machine health indicators obtained by signal processing, because unlike structural health monitoring, they are based more on changes in forcing functions, varying greatly between different machines and components, and not just on dynamic (i.e. modal) properties.
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为机械系统选择合适的信号处理工具
西蒙·布劳恩是最早认识到机械系统信号处理的特殊要求的人之一,这就是他创办《机械系统与信号处理》杂志的原因。机械工程师通常在信号处理方面没有受过很好的训练,所以信号处理专家通常是从其他领域招募的,例如电气工程、语音处理、声学,这些领域通常有不同的要求。信号处理在机械工程(和机电一体化)中的一个非常重要的应用领域是机器人和主动控制。这需要实时的因果处理,但这对结果造成了限制,因为因果滤波器具有较差的特性和相位失真。微分、积分和希尔伯特变换在时域中也存在问题。机械状态监测是机械工程师信号处理的另一个重要领域。本文表明,因果信号处理不仅不需要用于机器状态监测,甚至不需要用于关键机器的在线监测,而且还给出了非因果信号处理可以避免的问题和失真。本文说明了使用非因果处理(主要基于FFT(快速傅立叶变换)分析)获得的优势,用于零相移的理想滤波,以及通过频域的无误差微分/积分和希尔伯特变换。然而,(非因果)FFT算法的循环性会产生“环绕效应”,必须加以缓解。除了主动控制之外,本文还简要讨论了因果处理具有优势的情况,例如基于八度的滤波,以及作为FFT分析先驱的实时变焦。最后,本文讨论了通过信号处理获得的机器健康指标的特殊要求,因为与结构健康监测不同,它们更多地基于强迫函数的变化,在不同的机器和部件之间变化很大,而不仅仅是动态(即模态)特性。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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