The vibration spectrum is a feature used in several monitoring systems designed to diagnose many mechanical systems. The most usual way to obtain this feature is to input the time domain vibration data into a processor programmed with algorithms, such as the Fast Fourier Transform. Alternatively, this feature can be obtained more directly by using twin-microaccelerometers data and a simple electronic circuit. When compared to the spectrum calculation using the Fast Fourier Transform, the second strategy presents advantages related to the possibility to design microsensors with reduced size and low power consumption. However, the manufacturing process results into different physical parameters between the twin-accelerometers, and these differences raise the spectrum distortion. To overcome this drawback, in this work the tuning of the spectrum analyzer microdevice based on twin-microaccelerometers is proposed by adjusting the accelerometers actuation voltages amplitudes. To perform the tuning, three different variations of the Generalized Differential Evolution algorithm—an extension of Differential Evolution to solve multi-objective problems—with four boundary-handling strategies are used and their results are compared. The objective functions and constraints are based on the Fourier series composition of the spectrum analyzer system closed-loop gain—which depends on the actuation voltages. The advantages and disadvantages of applying this strategy are discussed in detail, as well as the results obtained for the Pareto-set approximation. The results obtained in MATLAB® simulations—specially the distortion-sensitivity compromise—are demonstrated, discussed, and validated.

振动频谱是在几个旨在诊断许多机械系统的监视系统中使用的功能。获得此功能的最常用方法是将时域振动数据输入到使用算法进行编程的处理器中，例如快速傅立叶变换。或者，可以通过使用双微加速度计数据和简单的电路更直接地获得此功能。当与使用快速傅立叶变换进行频谱计算相比时，第二种策略具有与设计尺寸更小，功耗更低的微传感器的可能性有关的优势。但是，制造过程导致双加速度计之间的物理参数不同，并且这些差异会引起频谱失真。为了克服这个缺点，在这项工作中，通过调整加速度计的激励电压幅度，提出了基于双微加速度计的频谱分析仪微设备的调谐方法。为了执行调整，使用了三种不同的通用差分进化算法变体（差分进化的扩展，以解决多目标问题）和四种边界处理策略，并对它们的结果进行了比较。目标函数和约束条件基于频谱分析仪系统闭环增益的傅立叶级数，该傅立叶级数取决于驱动电压。详细讨论了应用此策略的优缺点，以及对Pareto-set逼近获得的结果。演示了在MATLAB®仿真中获得的结果-特别是失真-敏感性折衷，