Sensornetzwerke zur Kugellagerdiagnose am rotierenden Innenring

Ullrich, Michel; Wolf, Maik; Rudolph, Mathias; Diller, Wolfgang; Root, Jurij

doi:10.1515/zwf-2021-0153

Cited by 1 publication

(2 citation statements)

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“…In this context, few solutions have realized an autonomous solution-based energy harvesting and wireless sensor node for IIoT devices in industry and predictive maintenance. In [20][21][22], a sensor network powered through an electromagnetic harvester was developed for the diagnosis of ball bearings; nevertheless, the results showed that the system required 100 mW to measure and transmit the information, which was quite high. Further, in [22], a wireless sensor node was developed for structural health monitoring and had the advantage of low power consumption.…”

Section: Introductionmentioning

confidence: 99%

“…In [20][21][22], a sensor network powered through an electromagnetic harvester was developed for the diagnosis of ball bearings; nevertheless, the results showed that the system required 100 mW to measure and transmit the information, which was quite high. Further, in [22], a wireless sensor node was developed for structural health monitoring and had the advantage of low power consumption. Nevertheless, the solution had a limitation in terms of the photovoltaic source, which led to a long charging time of 30 min.…”

Section: Introductionmentioning

confidence: 99%

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Vibration Converter with Passive Energy Management for Battery-Less Wireless Sensor Nodes in Predictive Maintenance

et al. 2022

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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.

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