Application of vibration signal in the diagnosis of IC engine valve clearance

The article describes a concept of a non-invasive method for diagnosing the size of valve clearance in internal combustion engines, based on the analysis of engine surface vibration signals using artificial neural networks. The applicability of the method was tested on a single-cylinder compression-ignition engine with a low power rating, which had an OHV timing gear, acting indirectly on the valves, and manual adjustment of valve clearance. The method uses as diagnostic signals the readings of vibration sensors, which record the acceleration of engine head movement as a function of the angle of rotation of the crankshaft, with pre-set valve clearance values measured in a cold condition. From among the signals recorded, components corresponding to the impact of rocker arms against valve stems were identified and low-pass filtered in order to eliminate measurement interference. A classifier of selected features of the signals processed was constructed using artificial neural networks. This classifier recognizes signals generated by engines with correct valve clearance as well as those with too much and too little valve clearance.

[1]  Piotr Czech Intelligent Approach to Valve Clearance Diagnostic in Cars , 2013, TST.

[2]  Józef Jonak,et al.  Classification of wear level of mining tools with the use of fuzzy neural network , 2013 .

[3]  Yao WANG,et al.  MODY EMPIRYCZNE DLA CELÓW DETEKCJI USZKODZEŃ PRZEKŁADNI IMPROVEMENT OF LOCAL MEAN APPROXIMATION IN EMPIRICAL MODE DECOMPOSITION FOR GEAR FAULT DETECTION , 2010 .

[4]  Bogusław Łazarz,et al.  Condition monitoring of engine timing system by using wavelet packet decomposition of a acoustic signal , 2014 .

[5]  Figlus Tomasz,et al.  Assessment of the vibroactivity level of SI engines in stationary and non-stationary operating conditions , 2014 .

[6]  Łukasz JEDLIŃSKI,et al.  Multi-channel registered data denoising using wavelet transforM odszuMianie danych rejestrowanych wielokanałowo , 2012 .

[7]  Ł. Jedliński,et al.  Optimum choice of signals' features used in toothed gears' diagnosis , 2010 .

[8]  Andrew Ball,et al.  Detection of engine valve faults by vibration signals measured on the cylinder head , 2006 .

[9]  Zhenyuan Zhong,et al.  Fault diagnosis for diesel valve trains based on time–frequency images , 2008 .

[10]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[11]  Liu Jianmin,et al.  Fuel Injection System Fault Diagnosis Based on Cylinder Head Vibration Signal , 2011 .

[12]  Grzegorz Litak,et al.  Combustion timing variability in a light boosted controlled auto-ignition engine with direct fuel injection , 2013 .

[13]  G. Yen,et al.  Fault classification on vibration data with wavelet based feature selection scheme , 2006, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[14]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .

[15]  Liu Hong,et al.  A time domain approach to diagnose gearbox fault based on measured vibration signals , 2014 .

[16]  Jin Chen,et al.  Analysis of engine vibration and design of an applicable diagnosing approach , 2003 .

[17]  G. Peruń,et al.  Possibilities of Using Vibration Signals for the Identification of Pressure Level in Tires with Application of Neural Networks Classification , 2013 .

[18]  Rafał Burdzik Implementation of multidimensional identification of signal characteristics in the analysis of vibration properties of an automotive vehicle’s floor panel , 2014 .

[19]  Piotr CZECH,et al.  ApplicAtion of cepstrum And spectrum histogrAms of vibrAtion engine body for setting up the cleArAnce model of the piston-cylinder Assembly for rbf neurAl clAssifier , 2011 .

[20]  Rafał Burdzik,et al.  Application of Vibroacoustic Methods for Monitoring and Control of Comfort and Safety of Passenger Cars , 2013 .

[21]  M. Kronast Theory and application of modal analysis in vehicle noise and vibration refinement , 2010 .

[22]  Piotr Deuszkiewicz,et al.  Modeling of Powertrain System Dynamic Behavior with Torsional Vibration Damper , 2014 .

[23]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[24]  Zhen Song,et al.  Fault Diagnosis of Valve Train of Internal Combustion Engine Based on the Artificial Neural Network and Support Vector Machine , 2012 .

[25]  Jyoti K. Sinha,et al.  Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis , 2009 .

[26]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planetary gearboxes , 2012 .