This paper presents an effective bearing fault diagnosis model based on multiple extraction and selection techniques. In multiple feature extraction, the discrete wavelet transform, envelope analysis, and fast Fourier transform are considered. While the combined binary particle swarm optimization with extended memory is focusing on feature selection. The current signals are analyzed by discrete wavelet transform. From there, the statistical features in the time and frequency domain are extracted by two techniques: envelope analysis, fast Fourier transform. Subsequently, the binary particle swarm optimization is combined with extended memory and two proposed position update mechanisms to eliminate redundant or irrelevant features to achieve the optimal feature subset. Besides, three classifiers including naïve Bayes, decision tree, and linear discriminant analysis are applied and compared to select the best model to detect the bearing fault.
This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.
The task of accurately bearing fault diagnosis of the rotary machinery from the measured signal remains a major problem that attracts a lot of attention. This paper proposed a new approach to build an efficient bearing fault diagnostic model for rotary machinery. The model is based on the persistence spectrum image and convolutional neural network (CNN) with ResNet structure. The persistence spectrum is extracted from the envelope of the raw vibration signal. Then, the persistence spectrum image is constructed based on short-time Fourier transform, which presents a new relationship between the frequency, magnitude, and energy of each signal with time, which the traditional spectrum analysis methods have not been given before. To explore the discriminant features from the persistence spectrum image of the envelope signal, an improved CNN with ResNet structure allows direct connection feature maps from the lower-level layer to the higherlevel layer. That helps to exploit the granularity features in the low-level layer which can be lost when feedforward through adjacent layers in a traditional CNN. As a result, the proposed model operates efficiently with high accuracy not only under various working loads but also under noise conditions. Besides, its performance is very satisfactory compared to other types of two-dimensional images and other state-of-theart diagnosis models. Overall, the proposed approach is highly feasible for an intelligent bearing fault diagnostic model.
INDEX TERMSBearing fault diagnosis, convolutional neural network, persistence spectrum, ResNet.
This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.
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