Fault Detection For Diesel Engines Research Articles

Classification-Based Fuel Injection Fault Detection of a Trainset Diesel Engine Using Vibration Signature Analysis

Abstract Diesel engines are crucial components of trainsets. Automated fault detection of diesel engines can play an important role for increasing reliability of passenger trains. In this research, vibration-based fuel injection fault detection of a high-power 12-cylinder trainset diesel engine is studied. Vibration signals are analyzed in frequency and time-frequency domains to obtain possible patterns of faults. Fast Fourier transform (FFT) and wavelet packet transform (WPT) of vibration signals are used to extract several uncorrelated features. These features are chosen to increase the ability of classifiers to separate healthy and faulty engine sides, automatically. Different classification methods including multilayer perception (MLP), support vector machines (SVM), K-nearest neighbor (KNN), and local linear model tree (LOLIMOT) are used to process captured features; these methods are utilized in both “Single-sensor condition monitoring” and “Classification and fault detection” sections. It is shown that KNN networks are practical tools in the proposed fault detection procedure. The main novelty of this work comes from introducing a rich feature-extraction method based on a combination of FFT and db4 features. In addition, the complexity of computations and average running-time decrease while classification accuracy in the fuel injection fault detection procedure increases.

A Method Combining Order Tracking and Fuzzy C-Means for Diesel Engine Fault Detection and Isolation

Diesel engine works under variable speed conditions; fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In this paper, firstly, the acceleration signal of diesel engine is resampled by order tracking, in which the rotating speed is computed in every working cycle, and the order tracking spectrum is created in each interval’s speed; then different order band accumulated energy is computed as feature vector. After standardizing these features, the fuzzy c-means (FCM) is introduced to use them as input vector; the optimized classified matrix and clustering centers can be obtained using FCM iteration method; then the fault can be detected by calculating the approach degree between the unknown samples and the known ones. To validate the method, some experiments have been performed; the results show that the signal can be reconstructed, and the features of order band accumulated energy can reflect the information of different wear conditions in crank-shaft bearing; then the fault can be detected accurately. The method of nonentire work cycle is also introduced as a comparison with our method; the result shows our method has more accuracy classification.

An Approach on Fault Detection in Diesel Engine by Using Symmetrical Polar Coordinates and Image Recognition

Vibration technique provides useful information in fault detection of diesel engine, bringing significant cost benefits to diesel engine condition monitoring. Usually, time-frequency calculation on vibration signal is so complex that it is difficult to achieve online fault detection. In this paper, a method of fault detection in diesel engine is developed based on symmetrical polar coordinates and image recognition. In this method, time-domain waveform of vibration signal is transformed into snowflake-shaped in mirror symmetry pattern without time-frequency analysis. By the comparison of the geometric features of the snowflake images from different wear conditions of crankshaft bearing in diesel engines, we use centroid position and direction angle of the petal in snowflake image as features to detect the fault. Then, fuzzy c-means (FCM) are used to detect the conditions of the engine according to these features. In order to validate the methods, some experiments have been performed, the experimental results show that the centroid position and direction angle of the petal in snowflake image can reflect the information of different wear conditions in crankshaft bearing, and the fault of crankshaft bearing can be detected accurately. Hence, the method can work as fault detection in diesel engine, which is simple and effective, compared with time-frequency calculation method.