scholarly journals Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
ZhenHong Guan ◽  
Yajuan Gao
Keyword(s):  
Prediction Model ◽  
Information Fusion ◽  
Health Management ◽  
Vibration Signal ◽  
Fault Prediction ◽  
Order Structure ◽  
Vibration Prediction ◽  
Potential Failure ◽  
Change Trend ◽  
Full Vector

Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.

scholarly journals Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
Yongxiang Cui ◽  
Zhenhong Guan
Keyword(s):  
Data Acquisition ◽  
Information Fusion ◽  
Prediction Method ◽  
Fault Prediction ◽  
Vibration Measurement ◽  
Signal Acquisition ◽  
Vector Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Full Vector

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder

2020 ◽  
pp. 095440622094103
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Qiang Fu ◽  
Xiaomei Ni
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Data Augmentation ◽  
Time Series Prediction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Denoising Autoencoder ◽  
Operation Condition ◽  
Variational Autoencoder

Accurate fault prediction of rolling bearing can predict the operation condition in advance, which is an important means to ensure the safety and reliability of rotating machinery. Aimed at the data processing of rolling bearing vibration signal with multi-fault and long time series, an intelligent fault prediction model based on gate recurrent unit and hybrid autoencoder is proposed in this paper. Firstly, vibration signals of multi-faults are brought into multi-layer gate recurrent unit model for multi-step and multi-variable time series prediction. Secondly, variational autoencoder is used for data augmentation of fault samples. Thirdly, the augmented fault samples are brought into stacked denoising autoencoder for noise reduction and fault prediction. Finally, fault prediction results of rolling bearing can be achieved on the basis of gate recurrent unit and hybrid autoencoder of variational autoencoder and stacked denoising autoencoder. The bearing datasets of Case Western Reserve University are used to verify the effectiveness of the proposed method. Comparative experiment results show that the proposed fault prediction model has more accurate time series prediction result and higher fault prediction accuracy than other deep learning models. With 98.68% accuracy of fault prediction, the proposed fault prediction model can be taken as an effective tool for intelligent predictive maintenance of rolling bearing.

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

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