Methods of Frequency Estimation Applied in Internet of Things Technologies

In the rapidly evolving realm of technology, the Industrial Internet of Things (IIoT) has gained prominence, including vibrodiagnostics and industrial equipment health monitoring. Integrating wireless networks within IIoT introduces challenges, notably in a balance between the desired amount of data transmitted and the technical limitations dictated by the issue of autonomy of IIoT devices. When implementing vibration signal-based equipment monitoring, a critical decision arises: transmitting the entire signal for cloud computation or conducting local computations on the sensor for selective data transmission. This paper explores three frequency estimation methods - parabolic interpolation, demodulation, and the harmonic series - in the context of IIoT technologies. Demonstrating their efficacy on synthetic signals, we highlight their suitability for vibrodiagnostics, considering the computational constraints of decentralized IIoT systems. Parabolic interpolation offers efficiency, demodulation excels in real-time processing, and the harmonic series method proves robust in scenarios with limited resolution. The comparative analysis showcases their strengths and limitations, emphasizing the importance of informed decision-making in choosing a frequency estimation method tailored to the specific constraints of the evolving IIoT environment. This study empowers practitioners to enhance the reliability and performance of IIoT-enabled devices and systems through efficient frequency estimation methods.