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首页> 外文期刊>Mechanical systems and signal processing >Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals
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Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

机译：基于振动信号的非平稳特性的先进诊断系统，用于内燃机的活塞拍击故障

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摘要

Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.
机译：人工神经网络（ANN）有可能解决活塞拍击故障自动诊断的问题，但是成功应用ANN的关键问题是在各种发动机状况（不同速度）下通过大量数据对网络进行训练/负载情况（正常情况下以及具有不同位置/级别的故障）。另一方面，最新的仿真技术提供了一种有用的替代方法，因为可以轻松地探究间隙变化的影响，而无需借助切削金属，从而为ANN创建足够的训练数据。本文基于现有的一些简化的活塞拍击模型，使用先进的多体动态仿真软件来模拟不同速度/负载和间隙条件下的活塞拍击故障。同时，通过一系列实验验证并更新了仿真模型。提出了一种三级网络系统来诊断活塞故障：故障检测，故障定位和故障严重性识别。多层感知器（MLP）网络用于检测阶段和严重性/预后阶段，而概率神经网络（PNN）用于识别哪个气缸有故障。最终，证明了仅在模拟数据上训练的网络可以在实际测试中有效地检测活塞拍击故障，并且还可以识别故障的位置和严重性。

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