Abstract
A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, theStransform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.
Highlights
Engine is one of the core mechanical components in a wide range of industrial applications
A new feature extraction approach based on S transform [12] and two-dimensional nonnegative matrix factorization (2DNMF), which has been used for bearing fault diagnosis in our earlier work [13], is employed for engine fault diagnosis
In traditional Nonnegative Matrix Factorization (NMF), a 2D timefrequency representation is first transformed into a 1D vector, and the training databases are represented with n × m matrix V, each column of which contains n = pq nonnegative values of one of the m time-frequency representations
Summary
Engine is one of the core mechanical components in a wide range of industrial applications. A new feature extraction approach based on S transform [12] and two-dimensional nonnegative matrix factorization (2DNMF), which has been used for bearing fault diagnosis in our earlier work [13], is employed for engine fault diagnosis. That is called the “curse of dimensionality” problem, which is more apparent in small-sample-size cases Another disadvantage of NMF is that such a matrix-to-vector transform may cause the loss of some structure information hiding in original 2D time-frequency representations [13]. In traditional NMF, a 2D timefrequency representation is first transformed into a 1D vector, and the training databases are represented with n × m matrix V, each column of which contains n = pq nonnegative values of one of the m time-frequency representations. Where Dk is a d1 × d2 matrix, which can be regarded as a reduced dimension representation of TFR Ak and can be used as features for fault diagnosis of engine states
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