scholarly journals An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

2020 ◽  
Vol 10 (1) ◽  
pp. 346
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Prediction Method ◽  
Vital Role ◽  
Remaining Useful Life ◽  
Statistical Characteristics ◽  
Adaboost Algorithm ◽  
Maximal Information Coefficient ◽  
Degradation Indicator

The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

scholarly journals Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 944
Author(s):  
Nannan Zhang ◽  
Lifeng Wu ◽  
Zhonghua Wang ◽  
Yong Guan
Keyword(s):  
Naive Bayes ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Remaining Useful Life ◽  
Statistical Characteristics ◽  
Mechanical Equipment ◽  
Weibull Distributions ◽  
Health States ◽  
Testing Stage ◽  
Useful Life

Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.

scholarly journals Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model

2020 ◽  
Vol 10 (6) ◽  
pp. 2056 ◽  
Author(s):  
Jingli Yang ◽  
Yongqi Chang ◽  
Tianyu Gao ◽  
Jianfeng Wang
Keyword(s):  
Unscented Kalman Filter ◽  
Failure Prediction ◽  
Prediction Method ◽  
Degradation Process ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Ar Model ◽  
Chebyshev Inequality ◽  
Intrinsic Mode Functions ◽  
Maximal Information Coefficient

A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions (IMFs) and a residual (Res), and the metric of maximal information coefficient (MIC) is used to select eligible IMFs to reconstruct signals. Then, the approximate entropy (ApEn)-weighted energy value of the reconstructed signals are calculated to track the degradation process of the rotating machinery. Furthermore, the Chebyshev inequality is introduced to determine the prediction starting time (PST). Finally, the auto regress (AR) model and unscented Kalman filter (UKF) algorithm are used to predict the remaining useful life (RUL) of the rotating machinery. The method is fully evaluated in a test-to-failure experiment. The obtained results show that the proposed method outperforms its counterparts on failure prediction of the rotating machinery.

scholarly journals A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Yongsheng Wang ◽  
Jing Gao ◽  
Zhiwei Xu ◽  
Jidong Luo ◽  
Leixiao Li
Keyword(s):  
Wind Energy ◽  
Prediction Model ◽  
Wind Power ◽  
Output Power ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term Memory ◽  
Evaluation Metrics ◽  
Power Prediction ◽  
Short Term

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

scholarly journals A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Han Wang ◽  
Gang Tang ◽  
Youguang Zhou ◽  
Yujing Huang
Keyword(s):  
Short Term Memory ◽  
Prediction Method ◽  
Health Indicators ◽  
Performance Degradation ◽  
Statistical Characteristics ◽  
Health Indicator ◽  
Trend Prediction ◽  
Lstm Network ◽  
Novel Strategy ◽  
The Stability

As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

2021 ◽  
Vol 16 (4) ◽  
pp. 35-51
Author(s):  
Bingchun Liu ◽  
Xiaogang Yu ◽  
Qingshan Wang ◽  
Shijie Zhao ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Optimal Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Concentration Prediction

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

scholarly journals Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bingqian Fan ◽  
Xuanxuan Xing
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Short Term Memory ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

scholarly journals A Prediction Method for the RUL of Equipment for Missing Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen Wenbai ◽  
Liu Chang ◽  
Chen Weizhao ◽  
Liu Huixiang ◽  
Chen Qili ◽  
...  
Keyword(s):  
Missing Data ◽  
Short Term Memory ◽  
Prediction Method ◽  
Score Function ◽  
Remaining Useful Life ◽  
Binary Matrix ◽  
Sample Distribution ◽  
Engineering Environment ◽  
Useful Life ◽  
Long Short Term Memory

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

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