Abstract
Predictive maintenance is becoming increasingly important in industry and requires continuous monitoring to prevent failures and anticipate maintenance processes, resulting in reduced downtime. Vibration is often used for failure detection and equipment conditioning as it is well correlated to the machine’s operation and its variation is an indicator of process changes. In this context, we propose a novel energy-autonomous wireless sensor system that is able to measure without the use of batteries and automatically deliver alerts once the machine has an anomaly by the variation in acceleration. For this, we designed a wideband electromagnetic energy harvester and realized passive energy management to supply a wireless sensor node, which does not need an external energy supply. The advantage of the solution is that the designed circuit is able to detect the failure without the use of additional sensors, but by the Analog Digital Converter (ADC) of the Wireless Sensor Nodes (WSN) themselves, which makes it more compact and have lower energy consumption. The electromagnetic converter can harvest the relevant energy levels from weak vibration, with an acceleration of 0.1 g for a frequency bandwidth of 7 Hz. Further, the energy-management circuit enabled fast recharging of the super capacitor on a maximum of 31 s. The designed energy-management circuit 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. In the failure condition with a frequency of 13 Hz and an acceleration of 0.3 g, the super capacitor recharging time was estimated to be 24 s. The proposed solution was validated by implementing real failure detection scenarios with random acceleration levels and, alternatively, modus. The results show that the WSN can directly measure the harvester’s response and decide about the occurrence of failure based on its characteristic threshold voltage without the use of an additional sensor.
Highlights
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]
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 proposed solution enables the detection of failure at a low constant of 13 Hzofand lowand acceleration levels, which early-stage failure failure detection
Summary
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]. 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]. Ambient energy harvesting from the environment surrounding the WSN presents an interesting alternative This includes solar, vibration, and 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 [17], a nonlinear magnetic rolling pendulum-based electromagnetic principle was developed, which could work for low frequencies in the range lower than
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