A Wireless Sensor Network with Enhanced Power Efficiency and Embedded Strain Cycle Identification for Fatigue Monitoring of Railway Bridges

Abstract

Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the maintenance costs and reduces the competitiveness of wireless sensor networks. To overcome this drawback, a signal conditioning hardware was designed that is able to significantly reduce the energy consumption. Furthermore, the communication overhead is reduced to a sustainable level by using an embedded data processing algorithm that extracts the strain cycles from the raw data. Finally, a simple software triggering mechanism that identifies events enabled the discrimination of useful measurements from idle data, thus increasing the efficiency of data processing. The wireless monitoring system was tested on a railway bridge for two weeks. The monitoring system demonstrated a good reliability and provided high quality data.

Event-driven strain cycle monitoring of railway bridges using a wireless sensor network with sentinel nodes

“…Furthermore, because the detected event is a train that travels to the bridge, there is a significant amount of metal between sentinel nodes and monitoring nodes while the alarming process is on-going. Past field tests experience suggested that passing trains have a significant impact on the link quality between network nodes [17] which could affect the speed and reliability of alarm message delivery. TinyOS 2.x, [20] the operating system for WSNs that was used in this work, provides a built-in mechanism for disseminating small pieces of data (smaller than a single packet payload), which could be used for alarm message delivery.…”

“…In case that the threshold was exceeded, the second processing step identifies strain cycles of a record according to the algorithm described in Feltrin, Pepovic, Flouri, and Pietrzak. [17] It searches a data record for consecutive local extremal points (maxima and minima, Figure 9). Because in fatigue assessment methods, the impact of small amplitude cycles below the endurance limit is neglected, while processing the record, the algorithm simultaneously removes cycles that are smaller than a given threshold.…”

“…In order to evaluate the gain in terms of power consumption and battery lifetime of event-driven monitoring, a laboratory test was performed in which a monitoring node was FIGURE 14 Histogram with distribution of responding monitoring nodes FIGURE 15 Time distribution of recorded events FIGURE 16 Average battery voltage time evolution of sentinel, monitoring, and relay nodes operating permanently according to the monitoring policy described by Feltrin et al [17] This laboratory test underestimates the power consumption because data communication did not occur. However, due to the significant power consumption of strain sensing and the event sparsity of the field test, the power consumption due to communication was negligible.…”

Proof of concept of wireless TERS monitoring

“…• In 2014, Feltrin et al [4] developed and deployed a custom sensor node for railway bridges to predict fatigue from train traffic. Their custom sensor node was based on the TI MSP430 SoC, and a sub-GHz radio.…”

“…Key findings were that measurements were validated against wired sensors; ACKs were required to avoid data loss; and various measures including adapting transmit power could be used to reduce power consumption. • In 2014, Feltrin et al [21] developed and deployed a custom sensor node for railway bridges to predict fatigue from train traffic.…”