A pattern recognition artificial neural network method for random fatigue loading life prediction. (June 2017)

• Record Type: Journal Article
• Authors: Durodola, J.F.; Li, N.; Ramachandra, S.; Thite, A.N.
• Is Part Of: International journal of fatigue. Volume 99:Part 1(2017)
• Page Start: 55
• Page End: 67
• Publication Date: 2017-06
• Subjects: Random fatigue -- Frequency -- Time domain -- Artificial neural networks -- Dirlik; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2017.02.003
• Languages: English
• ISSNs: 0142-1123

Abstract

Abstract: Random vibration fatigue loading occurs in automotive, aerospace, offshore and indeed in many structural and machine components. The analysis of these types of problems is often carried out using either time domain or frequency domain methods. Time domain rainflow counting together with Miner's linear damage accumulation assumption is widely accepted as a method of rationalising stress amplitude and mean stress from random fatigue loading and the damage caused to the component. Frequency domain methods provide a faster alternative for the analysis of the same problem but the results are generally conservative compared to those obtained using time domain methods. This paper presents an artificial neural network (ANN) machine learning approach for the prediction of damage caused by random fatigue loading. The results obtained for ergodic Gaussian stationary stochastic loading is very encouraging. The method embodies rapid analysis as well as better agreement with rainflow counting method than existing frequency domain methods.