Reliability Evaluation and Prediction Method with Small Samples

Hongyan Dui, Xinghui Dong, J. Tao
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引用次数: 0

Abstract

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.
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小样本可靠性评估与预测方法
如何准确地评估和预测小样本组件的降解状态是一个关键和现实的问题。针对可靠性预测领域存在的部件退化状态未知、相关环境数据难以获取、样本量小等问题,提出了一种基于Cox模型和1D CNN-BiLSTM模型的可靠性评估与预测方法。以某典型装载机6个部件的历史故障数据为例,综合比较逻辑回归(LR)模型、支持向量机(SVM)模型和反向传播神经网络(BPNN)模型等可靠性评估模型,提出了一种基于小样本Cox模型的可靠性评估方法。在此基础上,以最小化预测误差为目标,提出了一种基于一维卷积神经网络-双向长短期记忆网络(1D CNN-BiLSTM)的可靠性预测方法。通过比较自回归综合移动平均(ARIMA)模型和多元线性回归(MLR)等典型时间序列预测模型,验证了该模型的适用性和有效性。实验结果表明,该模型对可靠性计划的制定和可靠性维护活动的实施具有一定的参考价值。
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来源期刊
CiteScore
3.80
自引率
6.20%
发文量
57
审稿时长
20 weeks
期刊介绍: IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.
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