Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN
About: This article is published in Journal of Intelligent and Fuzzy Systems.The article was published on 2021-01-01. It has received 1 citations till now. The article focuses on the topics: Fault (power engineering).
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TL;DR: In this paper , a novel Adaptive Faster Region Convolutional Neural Networks (AFRCNN) scheme has been proposed for automatic fault detection of stainless-steel plates, which comprises three phases: identification, detection, and recognition.
Abstract: In today’s world, Steel plates play essential materials for various industries like the national defense industry, chemical industry, automobile industry, machinery manufacturing, etc. However, some defects may occur in a few plates during the manufacture of stainless-steel plates which directly impact the quality of the stainless-steel plate. If the faulted plate detection can be done manually, then it leads to errors and a time-consuming process. Hence, a computerized automated system is necessary to detect the abnormalities. In this paper, a novel Adaptive Faster Region Convolutional Neural Networks (AFRCNN) scheme has been proposed for automatic fault detection of stainless-steel plates. The proposed AFRCNN scheme comprises three phases: identification, detection, and recognition. Primarily, the damaged plates are identified using Region Proposal Network and Fully Convolutional Neural Network functioning as a combined process under AFRCNN. In the next phase, the number corresponding to the particular plate is recognized through the standard Automated Plate Number Recognition approach with the support of the character recognition technique. The simulation results manifest that the proposed AFRCNN scheme obtains a superior classification accuracy of 99.36%, specificity of 99.24%, and F1-score of 98.18% as compared with the existing state-of-the-art schemes.
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References
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TL;DR: An automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing and shows that although Daubechies 44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelets-based processing.
Abstract: This paper introduces an automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing. Vibration signals recorded from two experimental set-ups were processed for gears and bearing conditions. Four statistical features were selected: standard deviation, variance, kurtosis, and fourth central moment of continuous wavelet coefficients of synchronized vibration signals (CWC-SVS). In this research, the mother wavelet selection is broadly discussed. 324 mother wavelet candidates were studied, and results show that Daubechies 44 (db44) has the most similar shape across both gear and bearing vibration signals. Next, an automatic feature extraction algorithm is introduced for gear and bearing defects. It also shows that the fourth central moment of CWC-SVS is a proper feature for both bearing and gear failure diagnosis. Standard deviation and variance of CWC-SVS demonstrated more appropriate outcome for bearings than gears. Kurtosis of CWC-SVS illustrated the acceptable performance for gears only. Results also show that although db44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelet-based processing.
263 citations
TL;DR: A normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions and results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.
Abstract: Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most important components in the rotating machinery. In real industries, it is common to face that the issues of severe data imbalance and distribution difference since the number of fault data is small and the equipments frequently change the working conditions according to the production. To accurately and automatically identify the conditions of rolling bearings, a normalized convolutional neural network is proposed for the diagnosis of different fault severities and orientations considering data imbalance and variable working conditions. First, the batch normalization is adopted as a novel application to eliminate feature distribution difference, which is the prerequisite for ensuring generalization ability under different working conditions. Then, a special model structure is established and the overall performances of the proposed model are optimized by iterative update, which combines the exponential moving average technology. Finally, the proposed model is applied to the fault diagnosis under different data imbalance cases and working conditions. The effectiveness of the proposed method is verified based on two popular experiment dataset, and the diagnosis performance is widely evaluated in different scenarios. Comparisons with other commonly used methods and related works on the same dataset demonstrate the superiority of the proposed method. The results show that the proposed method has excellent diagnosis accuracy and admirable robustness, and also has sufficient stability on the data imbalance.
185 citations
TL;DR: A new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result of the proposed ensemble transfer convolutional neural networks driven by multi-channel signals.
Abstract: Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.
166 citations
TL;DR: It is shown how feature representations learned with CNN on large-scale annotated gas turbine normal dataset can be efficiently transferred to fault diagnosis task with limited fault data.
152 citations
TL;DR: This study proposes a novel end-to-end fault diagnosis method based on fine-tuned VMD and convolutional neural network that is trained only on a healthy and single fault dataset, without the use of compound fault data in training.
Abstract: In the case of a compound fault diagnosis of rotating machinery, when two failures with unequal severity occur in distinct parts of the system, the detection of a minor fault is a complicated and challenging task. In this case, the minor fault is overshadowed by the more severe one, and the characteristics of the compound fault are prone to the more severe one. Generally, the proposed methods in the literature consider compound failure as an individual fault type and unrelated to the corresponding single faults, either at the different locations of a sensitive component or in two separate parts, such as the bearing and gear, with approximately the same fault severity. Considering these issues, this study proposes a novel end-to-end fault diagnosis method based on fine-tuned VMD and convolutional neural network (CNN). The main idea is that CNN is trained only on a healthy and single fault dataset, without the use of compound fault data in training. In the test stage of the CNN model, the intelligent method alarms an untrained compound fault state if acquired probabilities of CNN output satisfy a set of probabilistic conditions. The performance of the fine-tuned VMD and the proposed hybrid method is evaluated by the decomposition of a simulated vibration signal and the analysis of a gearbox system with a compound fault scenario in such a way that one fault is minor and the other severe. The results obtained show the high accuracy of the proposed method in compound fault diagnosis and the feature extraction and classification of a minor fault in the presence of a more severe one.
99 citations