Abstract
Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF.
Highlights
With the rapid development of technology, industrial Internet of Things (IoT) and data driven techniques [1] have been revolutionizing manufacturing
As weight matrix is closely linked with the output features, heuristically we propose utilizing L1 norm to directly constrain the sparsity of the weight matrix and further constrain the sparsity of the feature matrix
As sparse filtering achieves the sparsity of the feature matrix by implementing L1 norm in the objective function, we utilize L1 regularization to constrain the sparsity of the weight matrix
Summary
With the rapid development of technology, industrial Internet of Things (IoT) and data driven techniques [1] have been revolutionizing manufacturing. Condition monitoring systems are adopted to collect the massive amount of data from monitored machines [4, 5]. With substantial development of computing systems and sensors, machinery fault diagnosis has fully embraced the extensive revolution in modern manufacturing. There are mainly three sequential steps in the framework of fault diagnosis systems: 1) signal acquisition; 2) feature extraction and selection and 3) fault classification [2, 6, 7]. Vibration signals are widely used as the data source since essential information about machine health condition is embedded in them. Representative features are extracted for fault classification. The more discriminative features are extracted from vibration signals, the more accurate fault diagnosis result would be.
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