Immune Continuous Memory Fault Diagnosis Method

doi:10.3901/JME.2023.24.034

Abstract

Abstract: The lack of fault samples and the difficulty of obtaining them are the key problems that restrict the development of intelligent diagnosis systems. Based on the numerous intelligent mechanisms of biological immune system, such as cloning, mutation and continuous learning, an immune continuous memory algorithm (ICM) with the ability of online continuous learning of new fault samples is proposed. This algorithm uses Gaussian function to calculate the probability of the occurrence of faulty samples (antigens) at any point in space, which is called the cytokine concentration. The concept of scale-variable B cells (SVB) is proposed according to the concentration of cytokines and the mechanism of biological immune clonal variation is simulated to form mature B cells with more number but stronger recognition ability for any antigen. Then a memory cell optimization strategy is proposed to optimize the number of mature B cells to form a small population of memory B cells capable of recognizing all training antigens for data classification. The condition update mechanism of memory B cell population is constructed to continuously learn new antigens and the memory B cell set with stronger recognition ability is formed to realize the continuous learning function of ICM. Simulation results on standard datasets show that the proposed ICM algorithm has better classification performance than other algorithms under the same conditions. The experimental results show that the ICM algorithm can improve the diagnostic performance by constantly updating memory cells and it is an effective continuous learning fault algorithm.

Key words: fault diagnosis, continuous learning, biological immune system, optimizing memory cells

CLC Number:

TP306

ZHANG Hongli, LAN Chao, LIU Shulin, XIAO Haihua, JIANG Lunchang, SUN Xin. Immune Continuous Memory Fault Diagnosis Method[J]. Journal of Mechanical Engineering, 2023, 59(24): 34-45.

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