The air conditioning compressor-piping system operates under varying load conditions for an extended period, leading to fatigue damage in the piping, which can fail the entire air conditioning system. Therefore, fatigue life analysis of the air conditioning compressor-piping system is crucial in its overall development. To more accurately estimate the fatigue life of the air conditioning compressor-piping system under varying conditions, a fatigue life prediction method is proposed. This method involves loading the identified compressor load time history into a finite element model in ANSYS, conducting time history analysis to determine the fatigue critical nodes, obtaining stress spectra and fatigue characteristic curves (S-N curves) for these critical nodes, and predicting the fatigue life using the three-parameter rainflow counting method and the Miner cumulative damage theory based on acceleration factors. Additionally, strain signals are collected in the vicinity of the critical nodes to calculate fatigue damage. Through experimentation and simulation, the analysis results reflect the fatigue life of the air conditioning compressor-piping system within a certain margin of error.
In order to accurately evaluate the residual life of rotating machinery equipment and grasp the health status information of bearings, a residual life prediction method based on SSA-BP was proposed. Firstly, Sparrow Search Algorithm (SSA) is used to optimize the connection weights and thresholds of BP neural network for selective optimization. This can solve the problem that the performance of BP neural network is greatly affected by connection weights and thresholds. At the same time, the life characteristic index is established by the method of eigenvalue extraction. The lifetime characteristic index was used as input neuron of SSA-BP network model. It establishes SSA-BP residual life prediction model. The feasibility of the model was verified by taking the experimental data of bearing life cycle from Xi 'an Jiaotong University as an example. The prediction curve of the remaining life of the bearing is given. Compared with the BP prediction model, the results show that the SSA-BP model can effectively reduce the prediction error of BP model.
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