Detection of engine valve faults by vibration signals measured on the cylinder head (original) (raw)
Related papers
2011
This paper studies condition monitoring technique of a small four strokes, single cylinder petrol engine using vibration signal analysis based on time domain, crank angle domain, and signal energy. Vibration signals are acquired from the cylinder head of the engine and used to describe engine processes such as intake/exhaust valve operations, ignition process, and combustion process. In this study, vibration signals have been applied to monitor various fault conditions in the engine such as intake and exhaust valve clearance faults. Vibration signals acquired in time domain could be mapped onto crank angle domain using top dead center signal. Time domain techniques were used to analyze vibration signals so that the main events related to the engine operations could be described easily. Using energy analysis technique, all fault conditions could be also identified. For future work, signal analysis techniques must be developed and the detected signals should be compared with other sig...
Fault diagnosis of the rotary machines is investigated through different kinds of signals. However, the literature shows that the vibration signal analysis is the most commonly used and effective approach. This research investigates the engine faults, including the misfire and valve clearance faults, using the vibration data captured by four sensors placed in different locations of the automobile engine and under different experimental circumstances. The application of the Fast Fourier Transform (FFT) is proposed as a feature extraction methodology which leads to the extraction of 16 features. In addition, four features are extracted using the acquired signals eigenvalues. The statistical approach is proposed to select features for classification of the engine's state. The Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k Nearest Neighbor (kNN) classification algorithms are employed to predict if the motor works healthily based on the selected features and, if not, what kind of faults is in the engine. The performance of ANN, SVM, and kNN in fault diagnosis is analyzed considering different scenarios, features, and based on multiple performance metrics. Comparing the results with the similar efforts in the literature proves the validity of the proposed methods and highlights their superiorities.
Cylinder pressures and vibration in internal combustion engine condition monitoring
Proceedings of Comadem 99 …, 1999
We focus on the detection of incipient faults in an internal combustion engine using a minimum number of sensory information. Inducing several faults in a 4 stroke diesel engine, cylinder pressure (P) and vibration (V) data are acquired. Two sets of artificial neural nets (ANN) are trained separately, using features from the pressure and vibration data. Both sets of nets show very good fault detection capabilities, thus demonstrating an alternative to the multi-sensory approach commonly adopted in fault diagnosis. In a separate study, P and V are fused together at the signal level and then used to train another set of ANNs which is shown to exhibit better reliability than either system. In the final study, the outputs of the 3 systems (P, V and fused P and V), are combined together in a majority voting system which outperforms all of its constituents in its diagnostic abilities, successfully identifying 2854 out of 3000 test cases with a confidence level of 90%.
Condition Monitoring and Diagnosis of an IC Engine using Vibration Recognition
Journal of emerging technologies and innovative research, 2021
This paper presents a model-based approach for the condition monitoring and fault diagnosis of an IC engine using the MATLAB software. The vibration signatures of normally aspirated engine contain valuable information about the health of the engine. So, to extract these features a Simulink model consisting of an engine and gearbox is constructed and then the vibrational data has been recorded using an accelerometer connection. Healthy and fault induced data are extracted and are further processed to match the input requirements of the MATLAB Software libraries.21 significant features in both the time and spectral domain have been extracted using the diagnostic feature designer. Which are then used to train the classifier which results in a function, which can be used to predict the nature of new untested data signals.
Application of vibration signal Kalman filtering to fault diagnostics of engine exhaust valve
Journal of Vibroengineering
A vibration signal of an internal combustion engine contains several diagnostic information, which are for the most part not utilised since they require complex processing methods in order to separate useful data and noise elimination. The method of diagnostics of leakages in a system: exhaust valve - cylinder, on the basis of reference model of the vibration signal is presented in the paper. It is proposed the statistical autoregressive model of signal with abstractive parameters. The recursive time-varing parameters estimation in the model is performed by the Kalman filtering. The model presented in the paper is illustrated with the signal of the spark ignition combustion engine Fiat Punto. The results of the analysis of the engine vibration signal under conditions of a defected exhaust valve and an increased valve clearance are presented. During road tests the vibrations acceleration signal was recorded as well as certain auxiliary signals used for a synchronisation and identific...
Vibration Analysis for Fault Detection of Automobile Engine Using PCA Technique
Today condition monitoring using vibration analysis is not only used for simple proposes like bearings and gears fault detection but also it is used in hybrid motors and cars in automobile industry for detecting or anticipating faults. The main aim of this paper is using vibration analysis in automobile industry to detect and categorize faults appeared due to poppet valve clearance and incomplete combustion sometimes called misfiring phenomenon in internal combustion engine. So we used four accelerometers on OHV engine body for registering vibration signals. Then using PCA technique acquiring data were investigated. At the end we succeeded to classify and detect faults with a high ratio of efficiency.
Multiscale analysis of vibration signals in engine valve system
Journal of Vibroengineering, 2015
The results of the research directed towards solving the task of automatic detection of wear and mechanical defects of internal combustion engines, are presented in the paper. The fractal geometry assumptions were applied in defining diagnostic features depended on the time scale of observations. Investigations were performed by means of the detrended fluctuation analysis. The scaling curves for the fluctuation function and singularity spectra of vibration signals obtained in monitoring a valve system of a spark ignition engine, were determined. The Hurst exponent, multifractality level and singularity exponent of a signal were tested as defects measures.
On-line diagnosis of mechanical defects of the combustion engine with principal components analysis
2015
Vibroacoustic diagnostics of combustion engines might be a complementary tool to OBD systems especially in mechanical defects detection. The presented method allows preliminary diagnosing of the mechanical defects on-line during driving without resampling the signal. The diagnosis is based on statistical features of the vibration signal. Principal components analysis (PCA) offers an approach for linear transformation of the problem variables so that the redundant information is reduced and the diagnostic model is more easily extracted. The product chosen for the investigation presented in this paper is a spark ignition engine. Performing analytical model, which is time consuming and costly, can supplement described method only in chosen cases. It is possible to diagnose of engine from the objective parameters of the specially prepared vibration signal without performing simulation models.
Vibration Analysis for Engine fault Detection
Journal of Robotics and Control (JRC)
In the Vibration analysis for engine fault detection, we use different visualization graph. Today's world growing fast and machinery part getting complex so it's difficult to find out fault in the machine so here means in this paper we explain how we find out the fault of the machine with help of visualization it's easy to find out a fault here we use angular.js, D3.js for visualization and use MQTT protocol for publishing and subscribe sensor data. In the automobile industries machines are the main part of how we find out fault yes we find out fault with help of sensors using sensors here we analyze the machine.
Part failure diagnosis for internal combustion engine using noise and vibration analysis
Periodica Polytechnica Transportation Engineering
Former practice proves that the vibration and noise caused by rotating or alternately moving parts together with the physical and chemical processes inside an internal combustion engine contain a great amount of information about what happens in-side the machine. The art of extracting useful information from time based vibro-acoustic signals is especially difficult in the case of an operating internal combustion engine because there are many vibration and noise sources and the rotation speed is not constant. The other problem is that the rotational speed is not constant in the case of an internal combustion engine during a single working cycle and in the course of continuous opera-tion. we developed a method to follow the fine details of angular speed fluctuations e. g. because of the activity of single piston. Our purpose was to develop a flexible and improvable measure-ment system which is usable both for experiments and final tests of several engines. Using the measuring set-up w...