## 1 Introduction
Experimental work has demonstrated that the fatigue life gauge can provide stable high-frequency fatigue responses repetitively. However, the commonly used fatigue life gauge cannot cover the low strain cycles, and it also has the drawbacks of low durability and nonlinear effects. Fatigue can be characterized by three parameters obtained from the SHM system: the number of cycles, the strain amplitude, and the state of stress. If the fatigue life at any given stress level and the number of cycles at the corresponding stress level are monitored, the aggregate life can be calculated using fatigue damage cumulative algorithms like the Palmgren-Miner linear rule [17]. Several efficient fatigue prediction methods have been developed based on the statistical measurements of the number of fatigue cycles, such as level cross counting, peak-valley value counting, simple range counting, rain-flow counting, and hysteresis loop counting. Among these methods, the rain-flow counting method is the most widely applied one. The basic concept of rain-flow counting method was initially introduced by [PERSON] and [PERSON] in 1968 to count the cycles or the half cycles of strain-time signals [18]. It was named for the way that rain runs off the roofs of pagodas. Counting is based on the stress-strain behavior of the material. The rain-flow counting was further improved by [PERSON] [19] making the algorithm more convenient for practical fatigue design and analysis. A modified rain-flow algorithm accounting for the load sequence effect has also been developed [20], and the regenerated loading histories can yield similar crack growth results using fracture mechanics-based models [21]. Many efforts have been directed to make it more comprehensive and sophisticated. For example, [PERSON] [22] proposed an easily programmed recursive algorithm. [PERSON] [23] developed an improved model of rain-flow for real-time counting in which rectification or modification of the stress-time history is no longer necessary before counting. This model was successfully applied to the data treatment of stress-spectra for high-speed Electric Multiple Units (EMUs). [PERSON] [24] enhanced the three-point rain-flow counting method and applied it to the evaluation of blasting damage. [PERSON] [25] replaced the commonly used records of wave heights employed in the design of marine structures with rain-flow matrices, and thus made an obvious improvement on the safety of the designed structures. In this paper, an intelligent system for fatigue damage assessment and fatigue life prediction of in-service structures is developed based on the online real-time SHM data. The system is inspired by the concept of tree ring data tracking. The rings of semi-tropical trees can clearly record the growth features of trees, such as irreversible age gaining, boundaries between each year, and speed of growth under various environmental conditions, _etc_. Similar phenomena are found when comparing the fatigue feature parameters (strain amplitude, number of cycles and state of stress) to that of the tree-rings data tracking. Thus, supported by fatigue loading spectrum theory, electronics and microprocessor technologies and wireless transmission technology, the intelligent system is designed and integrated. Laboratory tests based on strain gauges and PVDF sensors were set up to investigate the properties of the system.