## 7 Conclusions  
This paper proposes an autonomous WSN powered through an electromagnetic vibration converter with a passive energy management solution for predictive maintenance. The system is able to detect failure without the use of additional sensors, such as accelerometers or temperature sensors, which makes it more compact and have lower energy consumption. The proposed solution enables the detection of failure at a low constant frequency of 13 Hz and low acceleration levels, which enables early-stage failure detection. The proposed system consists of an electromagnetic converter-based planar spring that generated a maximum output voltage for the converter of up to 4 V peak-to-peak voltage for a coil resistance equal to 175 \(\Omega\) with an applied vibration of 13 Hz and 0.2 g in terms of frequency and acceleration, respectively. The reached output is stored in a super capacitor  
Figure 16: Identification of the time delay for the failure detection system.  
Figure 17: Failure detection in case of alternating between 0.2 g and 0.25 g acceleration: (**a**) acceleration level and (**b**) ADC input voltages.  
through an energy-management circuit, which consists of a six-stage voltage multiplier circuit connected to a wide-band DC-DC converter, as well as an under-voltage lock-out (UVLO) circuit to connect to the storage device to the WSN. Under the failure condition with a frequency of 13 Hz and an acceleration of 0.3 g, the charging time of the super capacitor when defined without any initial energy is about 14 min, where the recharging time is estimated to be 0.41 min. For a low acceleration level of 0.2 g, the startup time of the system is about 45 min and the recharging time is around 31 s. The last and crucial part, which is the sensor node, has good performance in terms of response time and data reliability. The wireless node operates at a frequency of 868 MHz, which helps to reduce energy consumption. It has a sensitivity of \(-\)116 dBm, which ensures a reliable and high data transmission rate. Once the solution starts working, only a few seconds up to 35 s is required to transmit the information in normal cases and up to 24 s in case of failure detection. Further, the proposed solution enables the detection of failure at low frequencies and acceleration, which enables early-stage failure detection. As a future perspective, the implementation of the solution for specific fault types and machine learning model will be conducted.  
[PERSON], [PERSON] and [PERSON] contributed equally to the conceptualization, investigation, methodology, data analysis, validation, and the original draft preparation and writing, [PERSON] contributed to the conceptualization, the original draft preparation and writing, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Bundesministeriums fur Wirtschaft und Energie (BMWi), within the project \"Entwicklung einer KI-gestutzten, miniaturisierten energieautarken Multisensor-platform als universelle IoT-Losung (KI-NO)\" (grant number 16 KN087924).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.