Abstract
The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, t-distributed stochastic neighbor embedding (t-SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and t-SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods.
Highlights
As the most essential system in rotating machinery, gear and bearing play a major role to keep the entire machine operating normally
In order to further illustrate the superiority of our method, several commonly used dimensionality reduction tools are adopted to combine with sparse filtering respectively for comparison analysis
Sparse filtering and -SNE were combined to determine the health conditions in an unsupervised way. By the both of gearbox and bearing experimental cases, it is demonstrated that the proposed method has the strong ability in feature extraction and classification
Summary
As the most essential system in rotating machinery, gear and bearing play a major role to keep the entire machine operating normally. If the hyperparameters are set improperly, it will produce a great impact on the diagnosis accuracy [13] These algorithms include sparse RBMs [14], sparse autoencoders [15], sparse coding [16], independent component analysis (ICA) [17] and others. Ngiam et al [19] proposed an unsupervised feature learning network named sparse filtering. It only focuses on optimizing the sparsity of the learned representations and ignores the problem of learning the data distribution. Because of its simplicity and performance, sparse filtering is proposed to solve fault diagnosis of rotating machines in this paper.
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