Fault diagnosis of rotating machinery based on dual convolutional-capsule network (DC-CN)

Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications. 

Capsule Network with Its Limitation, Modification, and Applications - A Survey

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.

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Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.

Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM

When submodule (SM) compound fault occurs in the Modular multilevel converter (MMC), the time-domain waveform characteristics of output current and internal circuiting current of MMC are not obvious, especially on the condition of high-level MMC. To address this issue, A compound fault diagnosis method based on an improved capsule network (CapsNet) is proposed in this paper. Firstly, The front end of the network adopts the feature extraction structure of One-dimensional Convolutional Neural Network (1DCNN) combined with Long and short-term memory network (LSTM). Then, the original data of MMC three-phase output currents and three-phase circulating currents are used as fault detection signal. The back-end employs a main capsule layer and a digital capsule layer structure, and realizes the transfer of feature vectors through a dynamic routing algorithm. This feature extraction structure combines the light weight of 1DCNN and the sequential sensitivity of LSTM. While ensuring that the information is fully extracted, the computational cost of the model is greatly reduced. Furthermore, the overlapping sampling method is used to construct and expand the sample sets. Compared with the other three common deep learning methods, their fault diagnosis performance under the condition of different levels and levels changing are analyzed respectively. The experimental results demonstrate that the proposed method has excellent cross-domain learning ability and high fault recognition accuracy. 

Compound Fault Diagnosis Method of Modular Multilevel Converter Based on Improved Capsule Network

In the prognostics health management (PHM) of rotating machinery, the accurate identification of bearing fault is critical. In recent years, various deep learning methods can well identify bearing fault based on monitoring data. However, facing changing operating conditions and noise pollution, the accuracy of these algorithms decreases significantly, which makes the algorithms difficult in practical applications. To solve this problem, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) is proposed for fault diagnosis. The proposed WMSCCN algorithm takes one-dimensional vibration signal as an input and no additional manual processing is required. In addition, the adaptive batch normalization (AdaBN) algorithm is introduced to further enhance the adaptability of WMSCCN under noise pollution and load changes. On generalization experiments under different loads, the proposed WMSCCN and WMSCCN-AdaBN algorithms achieve average accuracy rates of 96.44% and 97.44%, respectively, which is superior to other advanced algorithms. In the noise resistance experiment, the proposed WMSCCN-AdaBN can maintain a 92.3% diagnostic accuracy in a strong noise environment with a signal to noise ratio (SNR) = −4 dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels. Two experiments have effectively verified the validity and generalizability of the proposed model.

A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis

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A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis