Roller bearing fault discrimination with harmonic wavelet package and ORO-RVM

Roller bearing is one of the widely used elements in a rotary machine. The vibration signal of roller bearing reveals the characteristics and feature of roller bearing faults. Extraction feature from vibration signal and discrimination fault condition are indirect means to ensure the safety operation of machine. This paper addresses a novel roller bearing faults discrimination method with harmonic wavelet package and OAO-RVM (One Against One-Relevance Vector Machine). First, decompose vibration signal with harmonic wavelet package and compute the vector energy from wavelet coefficients. The feature vector is prepared after the vector energy has been standardized. Second, the multi-classification model is established with the simplified OAO-RVM for the purpose of identifying good bearing, bearing with inner race fault, bearing with out race fault and bearing with roller fault. Finally, capture the vibration signal from the roller bearing stand of electric engineering lab to illustrate the proposed method. The feature extraction method with harmonic wavelet package is compared with conventional wavelet package. The accuracy and efficiency of three fault discrimination methods are compared. Experiment results show that the proposed feature extraction method is more effective than conventional method. Compared with ORA (One Against Rest)-RVM and DT (Decision Tree)-RVM, the simplified ORO (One Against One)-RVM model is the best fault discrimination method for its accuracy and efficiency

The roller bearing fault diagnosis methods with harmonic wavelet packet and multi-classification relevance vector machine

Roller bearings are widely used elements in rotary machines. How to monitor the working conditions of roller bearings are focus study in the world. Monitoring the vibration signals of roller bearings is important indirect mean for that they reveal the characteristics and feature of roller bearing faults. Therefore, monitor the vibration signals and diagnose the working states of roller bearings are widely used to ensure the safety operation of the machines. This paper studies a novel roller bearing faults discrimination method with harmonic wavelet packet and Multi-classification Relevance Vector Machine (MRVM). Indeed, the fault discrimination is a pattern recognition process including feature extraction and faulty patterns recognition. Therefore, this paper collects vibration signals and decomposes them with harmonic wavelet packet. After the wavelet coefficients of each node are available, compute the vector energy by corresponding coefficients. The feature vector is prepared after the vector energy has been standardized. With MRVM, the paper proposes three fault discrimination methods in order to identify good bearing, bearing with inner race fault, bearing with outer race fault and bearing with roller fault. The Decision Tree (DT) model, One Against Rest (OAR) model and One Against One (OAO) model are used to propose the classification methods respectively. The proposed OAO model is simplified in order to improve the computation efficiency and simplify the architecture of the model. Finally, capture the vibration signal from the roller bearing stand of electric engineering lab and the roller bearing fault simulation stand QPZZ-II to illustrate the proposed methods. The proposed feature extraction method with harmonic wavelet packet is compared with conventional wavelet packet. With the previous feature vectors, the accuracy and efficiency of the three fault discrimination methods are compared. The accuracy and efficiency of three fault discrimination methods are compared under different conditions including developing faults, noise involving and several faults developing simultaneously. Experiment results show that the proposed feature extraction method is more effective than conventional method and the simplified OAO-RVM model possess the best fault discrimination accuracy and DT-RVM model possesses the better computation efficiency

Study on the Effect of Cuisine Tourism Resource on Tourists’ Willingness to Visit

This article aims at examining if tourists’ evaluation of cuisine tourism resource has a positive effect on their willingness to visit (WTV) the destination (H1). In Study 1, the content analysis of travelogues of 60 Chinese major tourist cities shows that the scenic spots have a significant effect on WTV, while the effect of cuisine tourism resource on WTV is not supported. Moreover, the tourist city Chengdu with both abundant scenic spots and cuisine resources is chosen for further research of how cuisine resources influence tourist’ decisions. In term of 276 questionnaires (Study 2) and 30 interviewee (Study 3), the results show that the impact of the cuisine resource on WTV is moderated by the tourists’ evaluation on the scenic spots. Only when tourists have a high evaluation on scenic spots, the cuisine resource plays a positive impact on WTV, showing the auxiliary attraction of cuisine resource to tourists

N-(2,3-Dimeth­oxy­benzyl­idene)naphthalen-1-amine

The title compound, C19H17NO2, represents a trans isomer with respect to the C=N bond. The dihedral angle between the planes of the naphthyl ring system and the benzene ring is 71.70 (3)°. In the crystal, weak C—H⋯O hydrogen bonding is present

Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

Time series is a special type of sequence data, a set of observations collected at even time intervals and ordered chronologically. Existing deep learning techniques use generic sequence models (e.g., recurrent neural network, Transformer model, or temporal convolutional network) for time series analysis, which ignore some of its unique properties. In particular, three components characterize time series: trend, seasonality, and irregular components, and the former two components enable us to perform forecasting with reasonable accuracy. Other types of sequence data do not have such characteristics. Motivated by the above, in this paper, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and apply it for the time series forecasting problem, namely \textbf{SCINet}. Compared to conventional dilated causal convolution architectures, the proposed downsample-convolve-interact architecture enables multi-resolution analysis besides expanding the receptive field of the convolution operation, which facilitates extracting temporal relation features with enhanced predictability. Experimental results show that SCINet achieves significant prediction accuracy improvement over existing solutions across various real-world time series forecasting datasets

Springback analysis of AA5754 after hot stamping: experiments and FE modelling

In this paper, the springback of the aluminium alloy AA5754 under hot stamping conditions was characterised under stretch and pure bending conditions. It was found that elevated temperature stamping was beneficial for springback reduction, particularly when using hot dies. Using cold dies, the flange springback angle decreased by 9.7 % when the blank temperature was increased from 20 to 450 °C, compared to the 44.1 % springback reduction when hot dies were used. Various other forming conditions were also tested, the results of which were used to verify finite element (FE) simulations of the processes in order to consolidate the knowledge of springback. By analysing the tangential stress distributions along the formed part in the FE models, it was found that the springback angle is a linear function of the average through-thickness stress gradient, regardless of the forming conditions used