A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features

On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

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“…Suppose there are C model classes and X = { x i ∈ R , i = 1 , 2 , ⋯ , N } are N n -dimensional training samples. 28–30…”

Section: Review Of Related Algorithmsmentioning

confidence: 99%

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

Shen

Song

et al. 2023

Structural Health Monitoring

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“…It effectively overcomes the shortcomings of traditional manual extraction of features, such as poor generalization ability and poor robustness, and reduces the uncertainty of traditional anomaly detection methods in the process of manual design and extraction. In recent years, different deep learning models, such as Deep Belief Networks (DBN), Stacked Autoencoder (SAE), Recursive Neural Network (RNN), and Conventional Neural Network (CNN), have received increasingly wide attention in intelligent anomaly detection [ 31 ]. Zhao et al [ 32 ] proposed an approach for multi-sensor fault detection based on DBN, using deep learning models for the classification and prediction of sensor faults.…”

Section: Related Workmentioning

confidence: 99%

Correlation-Based Anomaly Detection Method for Multi-sensor System

Han

Wang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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In industry, sensor-based monitoring of equipment or environment has become a necessity. Instead of using a single sensor, multi-sensor system is used to fully detect abnormalities in complex scenarios. Recently, physical models, signal processing technology, and various machine learning models have improved the performance. However, these methods either do not consider the potential correlation between features or do not take advantage of the sequential changes of correlation while constructing an anomaly detection model. This paper firstly analyzes the correlation characteristic of a multi-sensor system, which shows a lot of clues to the anomaly/fault propagation. Then, a multi-sensor anomaly detection method, which finds and uses the correlation between features contained in the multidimensional time-series data, is proposed. The method converts the multidimensional time-series data into temporal correlation graphs according to time window. By transforming time-series data into graph structure, the task of anomaly detection is considered as a graph classification problem. Moreover, based on the stability and dynamics of the correlation between features, a structure-sensitive graph neural network is used to establish the anomaly detection model, which is used to discover anomalies from multi-sensor system. Experiments on three real-world industrial multi-sensor systems with anomalies indicate that the method obtained better performance than baseline methods, with the mean value of F1 score reaching more than 0.90 and the mean value of AUC score reaching more than 0.95. That is, the method can effectively detect anomalies of multidimensional time series.

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“…After obtaining the variance of each normalized sample, the weight is allocated according to its internal and external quality. By sum the normalized sample with its weight, the results of data-level fusion are obtained in equation (8).…”

Section: Data-level Fusionmentioning

confidence: 99%

“…7 However, for complex working conditions with strong background noise, the single signal has insufficient capacity in weak crack detection. 8 As a result, it is urgent to make up for the limitations by fusing abundant information from multi sensors in data, feature, and decision level. 9 Besides, three types of fusion methods have different characteristics, which are applied in various conditions and reach different performances.…”

Section: Introductionmentioning

confidence: 99%

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.

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A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features

Cited by 37 publications

References 34 publications

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

Correlation-Based Anomaly Detection Method for Multi-sensor System

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

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