Fatigue Life Monitoring of Metallic Structures by Decentralized Rainflow Counting Embedded in a Wireless Sensor Network

Fatigue is one of the most widespread damage mechanisms found in metallic structures. Fatigue is an accumulated degradation process that occurs under cyclic loading, eventually inducing cracking at stress concentration points. Fatigue-related cracking in operating structures is closely related with statistical loading characteristics, such as the number of load cycles, cycle amplitudes and means. With fatigue cracking a prevalent failure mechanism of many engineered structures including ships, bridges and machines, among others, a reliable method of fatigue life estimation is direly needed for future structural health monitoring systems. In this study, a strategy for fatigue life estimation by a wireless sensor network installed in a structure for autonomous health monitoring is proposed. Specifically, the computational resources available at the sensor node are leveraged to compress raw strain time histories of a structure into a more meaningful and compressed form. Simultaneous strain sensing and on-board rainflow counting are conducted at individual wireless sensors with fatigue life prediction made using extracted amplitudes and means. These parameters are continuously updated during long-term monitoring of the structure. Histograms of strain amplitudes and means stored in the wireless sensor represent a highly compressed form of the original raw data. Communication of the histogram only needs to be done by request, dramatically reducing power consumption in the wireless sensing network. Experimental tests with aluminum specimens in the laboratory are executed for verification of the proposed damage detection strategy. INTRODUCTION Structural failures where fatigue damage has been cited as the root cause are widespread throughout modern history spanning from the Versailles train crash in 1842 [1] to the more recent destruction of China Airlines Flight 611 in 2002 [2]. Since fatigue is known to be a progressive material degradation, consideration of fatigue in a structural health monitoring (SHM) system is necessary. In quantifying fatigue damage by strain-time monitoring, a reliable fatigue life monitoring system can prove to be an invaluable tool that can improve structural management methods (i.e., inspection and maintenance) and potentially predict pending structural failure. In order for a fatigue monitoring system to be reliable, it first requires the ability to monitor all possible locations where fatigue induced cracking may occur. As it is nearly impossible to monitor all structural members, areas most susceptible to damage, particularly regions with high stress concentrations should be selected for monitoring. This may still require a dense network of sensors engaged in long-term monitoring. As a result, massive amounts of measured strain time history data are accumulated. Numerous wireless sensing platforms have emerged in the last decade for SHM [3]. Wireless sensors combine the functionality of data acquisition, embedded data analysis, and wireless data transmission within a single device. Among their many advantages, wireless sensing channels can be installed at a fraction of the cost of a wired channel. This allows dense sensor networks to be affordably deployed throughout large structural systems. This affordability