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首页> 外文期刊>WSEAS Transactions on Signal Processing >Multiple Fault Detection in typical Automobile Engines: a Soft computing approach
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Multiple Fault Detection in typical Automobile Engines: a Soft computing approach

机译：典型汽车发动机的多重故障检测：一种软计算方法

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摘要

Fault detection has gained growing importance for vehicle safety and reliability. For the improvement of reliability, safety and efficiency; advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many automobile systems. Many times, the trial and error approach has been applied to detect the fault and therefore engine may get more damaged instead of getting repaired. To alleviate such type of problem, the idea of sound recording of engines has been suggested to diagnose the fault correctly without opening the engine. In this paper, fault detection of two stroke engine, Hero Honda Passion four strokes and Maruti Suzuki Alto Automobile Engine have been proposed. The objective is to categorize the acoustic signals of engines into healthy and faulty state. Acoustic emission signals are generated from three different automobile engines in both healthy and faulty conditions. The paper proposes soft computing approach for detection of multiple faults in automobile engines which include signal conditioning, signal processing, statistical analysis and Artificial Neural Networks. The Statistical techniques and different Artificial Neural Networks have been employed to classify the faults correctly. Performance of Statistical techniques and ten types of Artificial Neural Networks have been compared on the basis of Average Classification Accuracy and finally, optimal Neural Network has been designed for the best performance.
机译：故障检测对于车辆安全性和可靠性越来越重要。为了提高可靠性，安全性和效率;对于许多汽车系统而言，先进的监督，故障检测和故障诊断方法变得越来越重要。很多时候，都采用试错法来检测故障，因此发动机可能会受到更大的损坏，而不是进行维修。为了减轻这种类型的问题，已经提出了发动机录音的想法，以在不打开发动机的情况下正确地诊断故障。本文提出了两冲程发动机，英雄本田激情四冲程和马鲁蒂铃木奥拓汽车发动机的故障检测。目的是将发动机的声音信号分类为健康和故障状态。在健康状态和故障状态下，都会从三种不同的汽车发动机中产生声发射信号。本文提出了一种软计算方法来检测汽车发动机中的多个故障，包括信号调节，信号处理，统计分析和人工神经网络。统计技术和不同的人工神经网络已被用来正确分类故障。在平均分类精度的基础上，比较了统计技术和十种类型的人工神经网络的性能，最后，设计了最佳神经网络以实现最佳性能。

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