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Fault Detection Method for Ship Equipment Based on BP Neural Network

机译：基于BP神经网络的船舶设备故障检测方法。

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摘要

Fault detection is of great importance for ship equipment's maintenance and repair, therefore, in this paper, we propose a novel fault detection method for ship equipment based on BP neural network. As the net error estimated by the ANN is lower than the current iteration, we use the back propagation algorithm to solve this problem. Hence, we introduce the BP neural network to detect fault for ship equipment. We take the VTC254P turbocharger as an example test the effectiveness, and six failures of turbocharger are utilized, such as oil leakage, surge, high temperature, abnormal vibration and noise, high pressure and insufficient pressure. Experimental results demonstrate that the proposed method is able to achieve higher performance on the accuracy of fault detection for turbocharger than other methods.
机译：故障检测对船舶设备的维护和修理具有重要意义，因此，本文提出了一种基于BP神经网络的船舶设备故障检测新方法。由于ANN估计的净误差低于当前迭代，因此我们使用反向传播算法来解决此问题。因此，我们引入了BP神经网络来检测船舶设备的故障。我们以VTC254P涡轮增压器为例，测试其有效性，并利用了涡轮增压器的六个故障，例如漏油，喘振，高温，异常振动和噪音，高压和压力不足。实验结果表明，与其他方法相比，该方法在涡轮增压器故障检测的准确性上具有较高的性能。

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