## 1 Introduction  
Predictive maintenance has become one of the key factors developed in modern industries to detect failure before it leads to catastrophic damage, which helps to reduce maintenance costs [1]. It can be associated with motors, conveyor chain failure, and mechanical component deterioration. Predictive maintenance can be achieved classically based on the expected lifetime of devices, which does not ensure the recognition of sudden damage. With industrial evolution, reliable and continuous system monitoring, especially potential failure prediction, is still quite challenging. It can be ensured through the continuous measurement of several parameters, such as temperature, vibration, pressure, and speed. To this end, various sensors are required, which lead to a large number of cables for data and power transmission [2]. Further, using such types of sensors limits their implementation, in particular, in inaccessible and harsh environments. Therefore, wireless sensor nodes (WSNs) in machine condition monitoring (MCM) are highly required to avoid catastrophic damages as well as enable easy control for the operating status of equipment in industry. Hence, to develop a reliable and autonomous WSN, several challenges occur nowadays. One of the most important challenges is the availability of a continuous and robust power source for the WSN. Battery technology is still used, but is not reliable and requires human intervention. Therefore, ambient energy harvesting from the environment surrounding the WSN presents an interesting alternative. This includes solar, vibration, and thermoelectric sources. Nevertheless, the challenge is to ensure a continuous and relevant energy amount. Even though solar sources can provide a considerable energy amount, it becomes limited, especially in indoor applications [2; 3]. For thermoelectric sources, several research works have shown that the conversion efficiency is very limited [4; 5]. To this end, the vibration source is most promising in industrial applications due to its presence in machines and its relatively high energy density [6]. In this case, several principles can be used to harvest energy from vibration, including electrostatic [7; 8], piezoelectric [9; 10], electromagnetic [11; 12], and magnetoelectric [13; 14] principles. Electromagnetic converters are the most considered in industrial applications due to their robustness, relatively relevant energy output, and their ease of integration compared to the other principles [6]. The main challenge for electromagnetic converters is generating relevant energy outcomes from weak vibration sources with low acceleration limited to 0.1 g to 0.2 g and low frequencies up to 30 Hz. Until now, in the state of the art, the developed harvesters for such vibration sources have several limitations. In [15], an eccentric pendulum-based electromagnetic converter was developed to harvest energy from 0.1 g acceleration and a frequency of 2 Hz, which was successful, but the total volume of the harvester was 153.9 cm\({}^{3}\), which results in a bulky system. Further, in [16], a compact solution with a total volume of \(\sim\)27.4 cm\({}^{3}\) was developed for a low frequency of 10 Hz, but was unable to generate energy under 1 g of applied acceleration. In [17], a nonlinear magnetic rolling pendulum-based electromagnetic principle was developed, which could work for low frequencies in the range lower than 30 Hz and an acceleration of 0.2 g; nevertheless, the proposed solution was difficult to be implemented easily in industrial applications. To this end, some researchers focused on rotational electromagnetic converters for low frequencies, as in [18], where, unfortunately, a high displacement of 20 mm was required to generate energy, and in [19], where the solution could work at low displacement, but presented a bulky solution with a total volume of 97 cm\({}^{3}\).