Rotate Vector Reducer Crankshaft Fault Diagnosis Using Acoustic Emission Techniques

An, Haipeng; Liang, Wei; Zhang, Yinlong; Li, Yang; Liang, Yanyan; Tan, Jindong

doi:10.1109/es.2017.55

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…In the fourth stage, which is dynamic routing, the similarity between the predicted feature vector 𝑢 | and the output feature vector 𝑣 is calculated by the inner product to optimize the update 𝑏 | , as shown in Formula (12).…”

Section: Capsule Network and Dynamic Routing Algorithmmentioning

confidence: 99%

“…In acoustic emission technology, Liang [2] uses a wavelet transform to denoise the acoustic emission signal, and predicts the failure trend of the RV reducer by using a hidden Markov model. An [12] carried out time-frequency feature extraction of acoustic emission signals of an RV reducer at different speeds and working conditions, and used these time-frequency features to qualitatively evaluate the crankshaft wear effect. Yang [13] combined compressed sensing and wavelet energy pooling to extract the fault features of RV acoustic emission signals and implemented a single fault classification of planetary wheel wear and sun wheel wear in an RV reducer.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers

Liu

Yang

et al. 2022

Sensors

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In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults in the absence of compound fault training data. In an examination of the rotate vector (RV) reducer, a core component of industrial robots, this paper proposes a compound fault identification method that is based on an improved convolutional capsule network for compound fault diagnosis of RV reducers. First, one-dimensional convolutional neural networks are used as feature learners to deeply mine the feature information of a single fault from a one-dimensional time-domain signal. Then, a capsule network with a two-layer stack structure is designed and a dynamic routing algorithm is used to decouple and identify the single fault characteristics for compound faults to undertake the diagnosis of compound faults of RV reducers. The proposed method is verified on the RV reducer fault simulation experimental bench, the experimental results show that the method can not only diagnose a single fault, but it is also possible to diagnose the compound fault that is composed of two types of single faults through the learning of two types of single faults of the RV reducer when the training data of the compound faults of the RV reducer are missing. At the same time, the proposed method is used for compound fault diagnosis of bearings, and the experimental results confirm its applicability.

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Section: Capsule Network and Dynamic Routing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers

Liu

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The structure of RV reducers is very complex, with special structure and dynamic characteristics, which also leads to its complex dynamic response, thus increasing the frequency spectrum complexity of the vibration signal [ 3 , 4 ]. For weak fault signals, the characteristics of fault signals are very weak and easily immersed in the noise of other components, so they cannot be effectively diagnosed [ 5 ]. AE refers to the phenomenon that materials are deformed or fractured by external or internal forces, releasing stress-strain in the form of elastic waves [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Yang

Liu

et al. 2022

Sensors

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The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults.

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Evaluation of the Angular Misalignment on RV Reducer Based on Motor Current Signature Analysis

Zou

et al. 2023

Mechanisms and Machine Science

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Rotate Vector Reducer Crankshaft Fault Diagnosis Using Acoustic Emission Techniques

Cited by 12 publications

References 9 publications

An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers

An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Evaluation of the Angular Misalignment on RV Reducer Based on Motor Current Signature Analysis

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