A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN

Wang, Mengjiao; Wang, Wenjie; Zhang, Xinan; Iu, Herbert Ho-Ching

doi:10.3390/e24060751

Cited by 33 publications

(21 citation statements)

References 29 publications

(30 reference statements)

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“…They also discussed the impact of two coding methods and different network structures on diagnostic accuracy. Additionally, Wang et al [16] used MTF to convert the original time series into corresponding graphs and used CNN to extract the deep feature information in the graph to complete the rolling bearing fault diagnosis.…”

Section: B Methods Based On Two-dimensional Featuresmentioning

confidence: 99%

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Time Series Classification Based on Multi-Dimensional Feature Fusion

et al. 2023

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Time series classification is a key problem in data mining, most of existing classification methods directly extract one-dimensional data from one-dimensional features, which cannot effectively express the inter-relation between different time points. Besides, some classification methods extract two-dimensional features through encoding raw one-dimensional data into two-dimensional images, and part of information is lost due to the difference of encoding methods. How to make full use of one-dimensional and two-dimensional features to extract valuable information and integrate them in an optimal fashion remains a promising challenge. In this paper, we propose a multi-scale convolutional network to extract one-dimensional features from time series for obtaining more feature information based on multi-scale convolution kernels. Two-dimensional features are constructed in terms of two-dimensional image coding based on Gramian angular field, M arkov transition field and Recurrence plot (GMR) methods. We develop a multi-dimensional feature fusion approach leveraging Squeeze-and-Excitation (SE) and Self-Attention (SA) mechanism to effective fusing one-dimensional multi-scale features and two-dimensional image features in terms of weight setting. We conduct experimental verification based on 84 complete data traces from a typical UCR dataset in the field. Experimental results show that the accuracy of our proposed approach improves by 3.35% compared with existing benchmark methods. The Gradient-weighted Class Activation M apping (Grad-CAM ) visualization analysis method is adopted, where our proposed approach extracts more accurate features and effectively distinguishes different time series data categories.

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Section: B Methods Based On Two-dimensional Featuresmentioning

confidence: 99%

“…The MTF can encode the one-dimensional time series signal into a two-dimensional image according to the Markov process, and the two-dimensional image encoded by this method can well retain the time dependent and frequency structure of the time series signal [16]. Assume that a time series X = {x 1 , x 2 , .…”

Section: ) Markov Transition Fieldmentioning

confidence: 99%

Time Series Classification Based on Multi-Dimensional Feature Fusion

et al. 2023

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“…In order to reduce the interference of uncertainties and other factors, a Markov transition field (MTF) and convolutional neural network (CNN) were automatically combined after learning data features. The CNN extracts original time series and converts them into feature information in images, which is a diagnosis method with high accuracy and can be used for fault classification (Mengjiao Wang et al, 2022) [119]. See Table 12 for the comparison of these methods.…”

Section: Other Types Of Vibration Image Methodsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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“…Machine learning (ML), as a representative data-driven fault-diagnosis method, has a strong capability of fault information extraction and fault classification [ 8 ]. Among ML methods, many techniques for signal processing and information extraction in fault conditions have been reported and discussed in the past few years.…”

Section: Introductionmentioning

confidence: 99%

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

Gao²,

Zhang³

et al. 2022

Entropy

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Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts’ experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University’s (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods.

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A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN

Cited by 33 publications

References 29 publications

Time Series Classification Based on Multi-Dimensional Feature Fusion

Time Series Classification Based on Multi-Dimensional Feature Fusion

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

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