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Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models

机译：融合多种数据驱动模型的船用柴油机基于学习的磨损故障诊断

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Wear fault is one of the dominant causes for marine diesel engine damage which significantly influences ship safety. By taking full advantage of the data generated in engine operation, machine learning-based wear fault diagnostic model can help engineers to determine fault modes correctly and take quick action to avoid severe accidents. To identify wear faults more accurately, a multi-model fusion system based on evidential reasoning (ER) rule is proposed in this paper. The outputs of three data-driven models including an artificial neural network (ANN) model, a belief rule-based inference (BRB) model, and an ER rule model are used as pieces of evidence to be fused in decision level. In this paper, the fusion system defines reliability and importance weight of every single model respectively. A novel method is presented to determine the reliability of evidence by considering the accuracy and stability of every single model. The importance weight is optimized by genetic algorithm to improve the performance of the fusion system. The proposed machine learning-based diagnostic system is validated by a set of real samples acquired from marine diesel engines in operation. The test results show that the system is more accurate and robust, and the fault tolerant ability is improved remarkably compared with every single data-driven diagnostic model. (C) 2019 Elsevier B.V. All rights reserved.
机译：磨损故障是导致船用柴油机损坏的主要原因之一，该损坏严重影响了船舶安全。通过充分利用发动机运转中产生的数据，基于机器学习的磨损故障诊断模型可以帮助工程师正确地确定故障模式并迅速采取措施，以避免发生严重事故。为了更准确地识别磨损故障，本文提出了一种基于证据推理（ER）规则的多模型融合系统。三种数据驱动模型的输出，包括人工神经网络（ANN）模型，基于信念规则的推理（BRB）模型和ER规则模型，被用作在决策层融合的证据。在本文中，融合系统分别定义了每个模型的可靠性和重要性权重。提出了一种通过考虑每个模型的准确性和稳定性来确定证据可靠性的新方法。通过遗传算法优化重要性权重，以提高融合系统的性能。所提出的基于机器学习的诊断系统通过从运行中的船用柴油机获取的一组真实样本进行验证。测试结果表明，该系统更加准确，健壮，与每个数据驱动的诊断模型相比，其容错能力得到显着提高。 （C）2019 Elsevier B.V.保留所有权利。

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